Correct upgrade for 0.5.1 version

release/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.0'
+default_version = '0.5.1'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft

release/python/0.5.1/crankshaft/crankshaft/__init__.py

@@ -0,0 +1,6 @@
+"""Import all modules"""
+import crankshaft.random_seeds
+import crankshaft.clustering
+import crankshaft.space_time_dynamics
+import crankshaft.segmentation
+import analysis_data_provider

release/python/0.5.1/crankshaft/crankshaft/analysis_data_provider.py

@@ -0,0 +1,67 @@
+"""class for fetching data"""
+import plpy
+import pysal_utils as pu
+
+
+class AnalysisDataProvider:
+    def get_getis(self, w_type, params):
+        """fetch data for getis ord's g"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            result = plpy.execute(query)
+            # if there are no neighbors, exit
+            if len(result) == 0:
+                return pu.empty_zipped_array(4)
+            else:
+                return result
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_markov(self, w_type, params):
+        """fetch data for spatial markov"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            data = plpy.execute(query)
+
+            if len(data) == 0:
+                return pu.empty_zipped_array(4)
+
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_moran(self, w_type, params):
+        """fetch data for moran's i analyses"""
+        try:
+            query = pu.construct_neighbor_query(w_type, params)
+            data = plpy.execute(query)
+
+            # if there are no neighbors, exit
+            if len(data) == 0:
+                return pu.empty_zipped_array(2)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % e)
+            return pu.empty_zipped_array(2)
+
+    def get_nonspatial_kmeans(self, query):
+        """fetch data for non-spatial kmeans"""
+        try:
+            data = plpy.execute(query)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)
+
+    def get_spatial_kmeans(self, params):
+        """fetch data for spatial kmeans"""
+        query = ("SELECT "
+                 "array_agg({id_col} ORDER BY {id_col}) as ids,"
+                 "array_agg(ST_X({geom_col}) ORDER BY {id_col}) As xs,"
+                 "array_agg(ST_Y({geom_col}) ORDER BY {id_col}) As ys "
+                 "FROM ({subquery}) As a "
+                 "WHERE {geom_col} IS NOT NULL").format(**params)
+        try:
+            data = plpy.execute(query)
+            return data
+        except plpy.SPIError, err:
+            plpy.error('Analysis failed: %s' % err)

release/python/0.5.1/crankshaft/crankshaft/clustering/__init__.py

@@ -0,0 +1,4 @@
+"""Import all functions from for clustering"""
+from moran import *
+from kmeans import *
+from getis import *

release/python/0.5.1/crankshaft/crankshaft/clustering/getis.py

@@ -0,0 +1,50 @@
+"""
+Getis-Ord's G geostatistics (hotspot/coldspot analysis)
+"""
+
+import pysal as ps
+from collections import OrderedDict
+
+# crankshaft modules
+import crankshaft.pysal_utils as pu
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# High level interface ---------------------------------------
+
+
+class Getis:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def getis_ord(self, subquery, attr,
+                  w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Getis-Ord's G*
+        Implementation building neighbors with a PostGIS database and PySAL's
+          Getis-Ord's G* hotspot/coldspot module.
+        Andy Eschbacher
+        """
+
+        # geometries with attributes that are null are ignored
+        # resulting in a collection of not as near neighbors if kNN is chosen
+
+        qvals = OrderedDict([("id_col", id_col),
+                             ("attr1", attr),
+                             ("geom_col", geom_col),
+                             ("subquery", subquery),
+                             ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_getis(w_type, qvals)
+        attr_vals = pu.get_attributes(result)
+
+        # build PySAL weight object
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate Getis-Ord's G* z- and p-values
+        getis = ps.esda.getisord.G_Local(attr_vals, weight,
+                                         star=True, permutations=permutations)
+
+        return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)

release/python/0.5.1/crankshaft/crankshaft/clustering/kmeans.py

@@ -0,0 +1,32 @@
+from sklearn.cluster import KMeans
+import numpy as np
+
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class Kmeans:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def spatial(self, query, no_clusters, no_init=20):
+        """
+            find centers based on clusters of latitude/longitude pairs
+            query: SQL query that has a WGS84 geometry (the_geom)
+        """
+        params = {"subquery": query,
+                  "geom_col": "the_geom",
+                  "id_col": "cartodb_id"}
+
+        data = self.data_provider.get_spatial_kmeans(params)
+
+        # Unpack query response
+        xs = data[0]['xs']
+        ys = data[0]['ys']
+        ids = data[0]['ids']
+
+        km = KMeans(n_clusters=no_clusters, n_init=no_init)
+        labels = km.fit_predict(zip(xs, ys))
+        return zip(ids, labels)

release/python/0.5.1/crankshaft/crankshaft/clustering/moran.py

@@ -0,0 +1,208 @@
+"""
+Moran's I geostatistics (global clustering & outliers presence)
+"""
+
+# TODO: Fill in local neighbors which have null/NoneType values with the
+#       average of the their neighborhood
+
+import pysal as ps
+from collections import OrderedDict
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# crankshaft module
+import crankshaft.pysal_utils as pu
+
+# High level interface ---------------------------------------
+
+
+class Moran:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def global_stat(self, subquery, attr_name,
+                    w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I (global)
+        Implementation building neighbors with a PostGIS database and Moran's I
+         core clusters with PySAL.
+        Andy Eschbacher
+        """
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr_name),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        attr_vals = pu.get_attributes(result)
+
+        # calculate weights
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate moran global
+        moran_global = ps.esda.moran.Moran(attr_vals, weight,
+                                           permutations=permutations)
+
+        return zip([moran_global.I], [moran_global.EI])
+
+    def local_stat(self, subquery, attr,
+                   w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I implementation for PL/Python
+        Andy Eschbacher
+        """
+
+        # geometries with attributes that are null are ignored
+        # resulting in a collection of not as near neighbors
+
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        attr_vals = pu.get_attributes(result)
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
+                                         permutations=permutations)
+
+        # find quadrants for each geometry
+        quads = quad_position(lisa.q)
+
+        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+    def global_rate_stat(self, subquery, numerator, denominator,
+                         w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+        Moran's I Rate (global)
+        Andy Eschbacher
+        """
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", numerator),
+                              ("attr2", denominator)
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        numer = pu.get_attributes(result, 1)
+        denom = pu.get_attributes(result, 2)
+
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate moran global rate
+        lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
+                                             permutations=permutations)
+
+        return zip([lisa_rate.I], [lisa_rate.EI])
+
+    def local_rate_stat(self, subquery, numerator, denominator,
+                        w_type, num_ngbrs, permutations, geom_col, id_col):
+        """
+            Moran's I Local Rate
+            Andy Eschbacher
+        """
+        # geometries with values that are null are ignored
+        # resulting in a collection of not as near neighbors
+
+        params = OrderedDict([("id_col", id_col),
+                              ("numerator", numerator),
+                              ("denominator", denominator),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        numer = pu.get_attributes(result, 1)
+        denom = pu.get_attributes(result, 2)
+
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
+                                              permutations=permutations)
+
+        # find quadrants for each geometry
+        quads = quad_position(lisa.q)
+
+        return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
+
+    def local_bivariate_stat(self, subquery, attr1, attr2,
+                             permutations, geom_col, id_col,
+                             w_type, num_ngbrs):
+        """
+            Moran's I (local) Bivariate (untested)
+        """
+
+        params = OrderedDict([("id_col", id_col),
+                              ("attr1", attr1),
+                              ("attr2", attr2),
+                              ("geom_col", geom_col),
+                              ("subquery", subquery),
+                              ("num_ngbrs", num_ngbrs)])
+
+        result = self.data_provider.get_moran(w_type, params)
+
+        # collect attributes
+        attr1_vals = pu.get_attributes(result, 1)
+        attr2_vals = pu.get_attributes(result, 2)
+
+        # create weights
+        weight = pu.get_weight(result, w_type, num_ngbrs)
+
+        # calculate LISA values
+        lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
+                                            permutations=permutations)
+
+        # find clustering of significance
+        lisa_sig = quad_position(lisa.q)
+
+        return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
+
+# Low level functions ----------------------------------------
+
+
+def map_quads(coord):
+    """
+        Map a quadrant number to Moran's I designation
+        HH=1, LH=2, LL=3, HL=4
+        Input:
+        @param coord (int): quadrant of a specific measurement
+        Output:
+            classification (one of 'HH', 'LH', 'LL', or 'HL')
+    """
+    if coord == 1:
+        return 'HH'
+    elif coord == 2:
+        return 'LH'
+    elif coord == 3:
+        return 'LL'
+    elif coord == 4:
+        return 'HL'
+    else:
+        return None
+
+
+def quad_position(quads):
+    """
+        Produce Moran's I classification based of n
+        Input:
+        @param quads ndarray: an array of quads classified by
+          1-4 (PySAL default)
+        Output:
+        @param list: an array of quads classied by 'HH', 'LL', etc.
+    """
+    return [map_quads(q) for q in quads]

release/python/0.5.1/crankshaft/crankshaft/pysal_utils/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions for pysal_utils"""
+from crankshaft.pysal_utils.pysal_utils import *

release/python/0.5.1/crankshaft/crankshaft/pysal_utils/pysal_utils.py

@@ -0,0 +1,211 @@
+"""
+    Utilities module for generic PySAL functionality, mainly centered on
+      translating queries into numpy arrays or PySAL weights objects
+"""
+
+import numpy as np
+import pysal as ps
+
+
+def construct_neighbor_query(w_type, query_vals):
+    """Return query (a string) used for finding neighbors
+        @param w_type text: type of neighbors to calculate ('knn' or 'queen')
+        @param query_vals dict: values used to construct the query
+    """
+
+    if w_type.lower() == 'knn':
+        return knn(query_vals)
+    else:
+        return queen(query_vals)
+
+
+# Build weight object
+def get_weight(query_res, w_type='knn', num_ngbrs=5):
+    """
+        Construct PySAL weight from return value of query
+        @param query_res dict-like: query results with attributes and neighbors
+    """
+    # if w_type.lower() == 'knn':
+    #     row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
+    #     weights = {x['id']: row_normed_weights for x in query_res}
+    # else:
+    #     weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
+    #                         if len(x['neighbors']) > 0
+    #                         else [] for x in query_res}
+
+    neighbors = {x['id']: x['neighbors'] for x in query_res}
+    print 'len of neighbors: %d' % len(neighbors)
+
+    built_weight = ps.W(neighbors)
+    built_weight.transform = 'r'
+
+    return built_weight
+
+
+def query_attr_select(params):
+    """
+        Create portion of SELECT statement for attributes inolved in query.
+        Defaults to order in the params
+        @param params: dict of information used in query (column names,
+                       table name, etc.)
+            Example:
+            OrderedDict([('numerator', 'price'),
+                         ('denominator', 'sq_meters'),
+                         ('subquery', 'SELECT * FROM interesting_data')])
+        Output:
+          "i.\"price\"::numeric As attr1, " \
+          "i.\"sq_meters\"::numeric As attr2, "
+    """
+
+    attr_string = ""
+    template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
+
+    if 'time_cols' in params:
+        # if markov analysis
+        attrs = params['time_cols']
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": val, "alias_num": idx + 1}
+    else:
+        # if moran's analysis
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery',
+                              'num_ngbrs', 'subquery')]
+
+        for idx, val in enumerate(attrs):
+            attr_string += template % {"col": params[val],
+                                       "alias_num": idx + 1}
+
+    return attr_string
+
+
+def query_attr_where(params):
+    """
+      Construct where conditions when building neighbors query
+        Create portion of WHERE clauses for weeding out NULL-valued geometries
+        Input: dict of params:
+            {'subquery': ...,
+             'numerator': 'data1',
+             'denominator': 'data2',
+             '': ...}
+        Output:
+          'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
+        Input:
+        {'subquery': ...,
+         'time_cols': ['time1', 'time2', 'time3'],
+         'etc': ...}
+        Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
+          NULL AND idx_replace."time3" IS NOT NULL'
+    """
+    attr_string = []
+    template = "idx_replace.\"%s\" IS NOT NULL"
+
+    if 'time_cols' in params:
+        # markov where clauses
+        attrs = params['time_cols']
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % attr)
+    else:
+        # moran where clauses
+
+        # get keys
+        attrs = [k for k in params
+                 if k not in ('id_col', 'geom_col', 'subquery',
+                              'num_ngbrs', 'subquery')]
+
+        # add values to template
+        for attr in attrs:
+            attr_string.append(template % params[attr])
+
+        if 'denominator' in attrs:
+            attr_string.append(
+              "idx_replace.\"%s\" <> 0" % params['denominator'])
+
+    out = " AND ".join(attr_string)
+
+    return out
+
+
+def knn(params):
+    """SQL query for k-nearest neighbors.
+        @param vars: dict of values to fill template
+    """
+
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                              "FROM ({subquery}) As j " \
+                              "WHERE " \
+                                "i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                                "%(attr_where_j)s " \
+                              "ORDER BY " \
+                                "j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
+                              "LIMIT {num_ngbrs})" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+
+# SQL query for finding queens neighbors (all contiguous polygons)
+def queen(params):
+    """SQL query for queen neighbors.
+        @param params dict: information to fill query
+    """
+    attr_select = query_attr_select(params)
+    attr_where = query_attr_where(params)
+
+    replacements = {"attr_select": attr_select,
+                    "attr_where_i": attr_where.replace("idx_replace", "i"),
+                    "attr_where_j": attr_where.replace("idx_replace", "j")}
+
+    query = "SELECT " \
+                "i.\"{id_col}\" As id, " \
+                "%(attr_select)s" \
+                "(SELECT ARRAY(SELECT j.\"{id_col}\" " \
+                 "FROM ({subquery}) As j " \
+                 "WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
+                       "ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
+                       "%(attr_where_j)s)" \
+                ") As neighbors " \
+            "FROM ({subquery}) As i " \
+            "WHERE " \
+                "%(attr_where_i)s " \
+            "ORDER BY i.\"{id_col}\" ASC;" % replacements
+
+    return query.format(**params)
+
+# to add more weight methods open a ticket or pull request
+
+
+def get_attributes(query_res, attr_num=1):
+    """
+        @param query_res: query results with attributes and neighbors
+        @param attr_num: attribute number (1, 2, ...)
+    """
+    return np.array([x['attr' + str(attr_num)] for x in query_res],
+                    dtype=np.float)
+
+
+def empty_zipped_array(num_nones):
+    """
+        prepare return values for cases of empty weights objects (no neighbors)
+        Input:
+        @param num_nones int: number of columns (e.g., 4)
+        Output:
+        [(None, None, None, None)]
+    """
+
+    return [tuple([None] * num_nones)]

release/python/0.5.1/crankshaft/crankshaft/random_seeds.py

@@ -0,0 +1,11 @@
+"""Random seed generator used for non-deterministic functions in crankshaft"""
+import random
+import numpy
+
+def set_random_seeds(value):
+    """
+    Set the seeds of the RNGs (Random Number Generators)
+    used internally.
+    """
+    random.seed(value)
+    numpy.random.seed(value)

release/python/0.5.1/crankshaft/crankshaft/segmentation/__init__.py

@@ -0,0 +1 @@
+from segmentation import * 

release/python/0.5.1/crankshaft/crankshaft/segmentation/segmentation.py

@@ -0,0 +1,176 @@
+"""
+Segmentation creation and prediction
+"""
+
+import sklearn
+import numpy as np
+import plpy
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn import metrics
+from sklearn.cross_validation import train_test_split
+
+# Lower level functions
+#----------------------
+
+def replace_nan_with_mean(array):
+    """
+        Input:
+            @param array: an array of floats which may have null-valued entries
+        Output:
+            array with nans filled in with the mean of the dataset
+    """
+    # returns an array of rows and column indices
+    indices = np.where(np.isnan(array))
+
+    # iterate through entries which have nan values
+    for row, col in zip(*indices):
+            array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
+
+    return array
+
+def get_data(variable, feature_columns, query):
+    """
+        Fetch data from the database, clean, and package into
+          numpy arrays
+        Input:
+            @param variable: name of the target variable
+            @param feature_columns: list of column names
+            @param query: subquery that data is pulled from for the packaging
+        Output:
+            prepared data, packaged into NumPy arrays
+    """
+
+    columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
+
+    try:
+        data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
+            variable=variable,
+            columns=columns,
+            query=query))
+    except Exception, e:
+        plpy.error('Failed to access data to build segmentation model: %s' % e)
+
+    # extract target data from plpy object
+    target = np.array(data[0]['target'])
+
+    # put n feature data arrays into an n x m array of arrays
+    features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
+
+    return replace_nan_with_mean(target), replace_nan_with_mean(features)
+
+# High level interface
+# --------------------
+
+def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
+    """
+    Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
+    the function.
+
+        Input:
+            @param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
+            @param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
+            @param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
+            @param model_parameters: A dictionary containing parameters for the model.
+    """
+
+    clean_target = replace_nan_with_mean(target)
+    clean_features = replace_nan_with_mean(features)
+    target_features = replace_nan_with_mean(target_features)
+
+    model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
+    prediction = model.predict(target_features)
+    accuracy_array = [accuracy]*prediction.shape[0]
+    return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
+
+
+
+def create_and_predict_segment(query, variable, target_query, model_params):
+    """
+    generate a segment with machine learning
+    Stuart Lynn
+    """
+
+    ## fetch column names
+    try:
+        columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1  '.format(query=query))[0].keys()
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    ## extract column names to be used in building the segmentation model
+    feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
+    ## get data from database
+    target, features = get_data(variable, feature_columns, query)
+
+    model, accuracy = train_model(target, features, model_params, 0.2)
+    cartodb_ids, result = predict_segment(model, feature_columns, target_query)
+    accuracy_array = [accuracy]*result.shape[0]
+    return zip(cartodb_ids, result, accuracy_array)
+
+
+def train_model(target, features, model_params, test_split):
+    """
+        Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
+        Input:
+            @param target: 1D Array of the variable that the model is to be trianed to predict
+            @param features: 2D Array NSamples * NFeatures to use in trining the model
+            @param model_params: A dictionary of model parameters, the full specification can be found on the
+                scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
+            @parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
+    """
+    features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
+    model = GradientBoostingRegressor(**model_params)
+    model.fit(features_train, target_train)
+    accuracy = calculate_model_accuracy(model, features, target)
+    return model, accuracy
+
+def calculate_model_accuracy(model, features, target):
+    """
+        Calculate the mean squared error of the model prediction
+        Input:
+            @param model: model trained from input features
+            @param features: features to make a prediction from
+            @param target: target to compare prediction to
+        Output:
+            mean squared error of the model prection compared to the target
+    """
+    prediction = model.predict(features)
+    return metrics.mean_squared_error(prediction, target)
+
+def predict_segment(model, features, target_query):
+    """
+    Use the provided model to predict the values for the new feature set
+        Input:
+            @param model: The pretrained model
+            @features: A list of features to use in the model prediction (list of column names)
+            @target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
+    """
+
+    batch_size = 1000
+    joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
+
+    try:
+        cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
+            joined_features=joined_features,
+            target_query=target_query))
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    results = []
+
+    while True:
+        rows = cursor.fetch(batch_size)
+        if not rows:
+            break
+        batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
+
+        #Need to fix this. Should be global mean. This will cause weird effects
+        batch = replace_nan_with_mean(batch)
+        prediction = model.predict(batch)
+        results.append(prediction)
+
+    try:
+        cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
+    except Exception, e:
+        plpy.error('Failed to build segmentation model: %s' % e)
+
+    return cartodb_ids, np.concatenate(results)

release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/__init__.py

@@ -0,0 +1,2 @@
+"""Import all functions from clustering libraries."""
+from markov import *

release/python/0.5.1/crankshaft/crankshaft/space_time_dynamics/markov.py

@@ -0,0 +1,194 @@
+"""
+Spatial dynamics measurements using Spatial Markov
+"""
+
+# TODO: remove all plpy dependencies
+
+import numpy as np
+import pysal as ps
+import plpy
+import crankshaft.pysal_utils as pu
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+
+class Markov:
+    def __init__(self, data_provider=None):
+        if data_provider is None:
+            self.data_provider = AnalysisDataProvider()
+        else:
+            self.data_provider = data_provider
+
+    def spatial_trend(self, subquery, time_cols, num_classes=7,
+                      w_type='knn', num_ngbrs=5, permutations=0,
+                      geom_col='the_geom', id_col='cartodb_id'):
+        """
+            Predict the trends of a unit based on:
+            1. history of its transitions to different classes (e.g., 1st
+               quantile -> 2nd quantile)
+            2. average class of its neighbors
+
+            Inputs:
+            @param subquery string: e.g., SELECT the_geom, cartodb_id,
+              interesting_time_column FROM table_name
+            @param time_cols list of strings: list of strings of column names
+            @param num_classes (optional): number of classes to break
+              distribution of values into. Currently uses quantile bins.
+            @param w_type string (optional): weight type ('knn' or 'queen')
+            @param num_ngbrs int (optional): number of neighbors (if knn type)
+            @param permutations int (optional): number of permutations for test
+              stats
+            @param geom_col string (optional): name of column which contains
+              the geometries
+            @param id_col string (optional): name of column which has the ids
+              of the table
+
+            Outputs:
+            @param trend_up float: probablity that a geom will move to a higher
+              class
+            @param trend_down float: probablity that a geom will move to a
+              lower class
+            @param trend float: (trend_up - trend_down) / trend_static
+            @param volatility float: a measure of the volatility based on
+              probability stddev(prob array)
+        """
+
+        if len(time_cols) < 2:
+            plpy.error('More than one time column needs to be passed')
+
+        params = {"id_col": id_col,
+                  "time_cols": time_cols,
+                  "geom_col": geom_col,
+                  "subquery": subquery,
+                  "num_ngbrs": num_ngbrs}
+
+        query_result = self.data_provider.get_markov(w_type, params)
+
+        # build weight
+        weights = pu.get_weight(query_result, w_type)
+        weights.transform = 'r'
+
+        # prep time data
+        t_data = get_time_data(query_result, time_cols)
+
+        sp_markov_result = ps.Spatial_Markov(t_data,
+                                             weights,
+                                             k=num_classes,
+                                             fixed=False,
+                                             permutations=permutations)
+
+        # get lag classes
+        lag_classes = ps.Quantiles(
+            ps.lag_spatial(weights, t_data[:, -1]),
+            k=num_classes).yb
+
+        # look up probablity distribution for each unit according to class and
+        #  lag class
+        prob_dist = get_prob_dist(sp_markov_result.P,
+                                  lag_classes,
+                                  sp_markov_result.classes[:, -1])
+
+        # find the ups and down and overall distribution of each cell
+        trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
+
+        # output the results
+        return zip(trend, trend_up, trend_down, volatility, weights.id_order)
+
+
+
+def get_time_data(markov_data, time_cols):
+    """
+        Extract the time columns and bin appropriately
+    """
+    num_attrs = len(time_cols)
+    return np.array([[x['attr' + str(i)] for x in markov_data]
+                     for i in range(1, num_attrs+1)], dtype=float).transpose()
+
+
+# not currently used
+def rebin_data(time_data, num_time_per_bin):
+    """
+        Convert an n x l matrix into an (n/m) x l matrix where the values are
+         reduced (averaged) for the intervening states:
+          1 2 3 4    1.5 3.5
+          5 6 7 8 -> 5.5 7.5
+          9 8 7 6    8.5 6.5
+          5 4 3 2    4.5 2.5
+
+          if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
+
+        This process effectively resamples the data at a longer time span n
+         units longer than the input data.
+        For cases when there is a remainder (remainder(5/3) = 2), the remaining
+         two columns are binned together as the last time period, while the
+         first three are binned together for the first period.
+
+        Input:
+          @param time_data n x l  ndarray: measurements of an attribute at
+           different time intervals
+          @param num_time_per_bin int: number of columns to average into a new
+           column
+        Output:
+          ceil(n / m) x l ndarray of resampled time series
+    """
+
+    if time_data.shape[1] % num_time_per_bin == 0:
+        # if fit is perfect, then use it
+        n_max = time_data.shape[1] / num_time_per_bin
+    else:
+        # fit remainders into an additional column
+        n_max = time_data.shape[1] / num_time_per_bin + 1
+
+    return np.array(
+      [time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
+       for i in range(n_max)]).T
+
+
+def get_prob_dist(transition_matrix, lag_indices, unit_indices):
+    """
+        Given an array of transition matrices, look up the probability
+        associated with the arrangements passed
+
+        Input:
+        @param transition_matrix ndarray[k,k,k]:
+        @param lag_indices ndarray:
+        @param unit_indices ndarray:
+
+        Output:
+        Array of probability distributions
+    """
+
+    return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
+                     for i in range(len(lag_indices))])
+
+
+def get_prob_stats(prob_dist, unit_indices):
+    """
+        get the statistics of the probability distributions
+
+        Outputs:
+            @param trend_up ndarray(float): sum of probabilities for upward
+               movement (relative to the unit index of that prob)
+            @param trend_down ndarray(float): sum of probabilities for downward
+               movement (relative to the unit index of that prob)
+            @param trend ndarray(float): difference of upward and downward
+               movements
+    """
+
+    num_elements = len(unit_indices)
+    trend_up = np.empty(num_elements, dtype=float)
+    trend_down = np.empty(num_elements, dtype=float)
+    trend = np.empty(num_elements, dtype=float)
+
+    for i in range(num_elements):
+        trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
+        trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
+        if prob_dist[i, unit_indices[i]] > 0.0:
+            trend[i] = (trend_up[i] - trend_down[i]) / (
+              prob_dist[i, unit_indices[i]])
+        else:
+            trend[i] = None
+
+    # calculate volatility of distribution
+    volatility = prob_dist.std(axis=1)
+
+    return trend_up, trend_down, trend, volatility

release/python/0.5.1/crankshaft/requirements.txt

@@ -0,0 +1,5 @@
+joblib==0.8.3
+numpy==1.6.1
+scipy==0.14.0
+pysal==1.11.2
+scikit-learn==0.14.1

release/python/0.5.1/crankshaft/setup.py

@@ -0,0 +1,49 @@
+
+"""
+CartoDB Spatial Analysis Python Library
+See:
+https://github.com/CartoDB/crankshaft
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name='crankshaft',
+
+    version='0.5.1',
+
+    description='CartoDB Spatial Analysis Python Library',
+
+    url='https://github.com/CartoDB/crankshaft',
+
+    author='Data Services Team - CartoDB',
+    author_email='dataservices@cartodb.com',
+
+    license='MIT',
+
+    classifiers=[
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Mapping comunity',
+        'Topic :: Maps :: Mapping Tools',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 2.7',
+    ],
+
+    keywords='maps mapping tools spatial analysis geostatistics',
+
+    packages=find_packages(exclude=['contrib', 'docs', 'tests']),
+
+    extras_require={
+        'dev': ['unittest'],
+        'test': ['unittest', 'nose', 'mock'],
+    },
+
+    # The choice of component versions is dictated by what's
+    # provisioned in the production servers.
+    # IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
+    install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
+
+    requires=['pysal', 'numpy', 'sklearn'],
+
+    test_suite='test'
+)

release/python/0.5.1/crankshaft/test/fixtures/getis.json

@@ -0,0 +1 @@
+[[0.004793783909323601, 0.17999999999999999, 0.49808756424021061], [-1.0701189472090842, 0.079000000000000001, 0.14228288580832316], [-0.67867750971877305, 0.42099999999999999, 0.24867110969448558], [-0.67407386707620487, 0.246, 0.25013217644612995], [-0.79495689068870035, 0.33200000000000002, 0.21331928959090596], [-0.49279481022182703, 0.058999999999999997, 0.31107878905057329], [-0.38075627530057132, 0.28399999999999997, 0.35169205342069643], [-0.86710921611314895, 0.23699999999999999, 0.19294108571294855], [-0.78618647240956485, 0.050000000000000003, 0.2158791250244505], [-0.76108527223116984, 0.064000000000000001, 0.22330306830813684], [-0.13340753531942209, 0.247, 0.44693554317763651], [-0.57584545722033043, 0.48999999999999999, 0.28235982246156488], [-0.78882694661192831, 0.433, 0.2151065788731219], [-0.38769767950046219, 0.375, 0.34911988661484239], [-0.56057819488052207, 0.41399999999999998, 0.28754255985169652], [-0.41354017495644935, 0.45500000000000002, 0.339605447117173], [-0.23993577722243081, 0.49099999999999999, 0.40519002230969337], [-0.1389080156677496, 0.40400000000000003, 0.44476141839645233], [-0.25485737510500855, 0.376, 0.39941662953554224], [-0.71218610582902353, 0.17399999999999999, 0.23817476979886087], [-0.54533105995872144, 0.13700000000000001, 0.2927629228714812], [-0.39547917847510977, 0.033000000000000002, 0.34624464252424236], [-0.43052658996257548, 0.35399999999999998, 0.33340631435564982], [-0.37296719193774736, 0.40300000000000002, 0.35458643102865428], [-0.66482612169465694, 0.31900000000000001, 0.25308085650392698], [-0.13772133540823422, 0.34699999999999998, 0.44523032843016275], [-0.6765304487868502, 0.20999999999999999, 0.24935196033890672], [-0.64518763494323472, 0.32200000000000001, 0.25940279912025543], [-0.5078622084312413, 0.41099999999999998, 0.30577498972600159], [-0.12652006733772059, 0.42899999999999999, 0.44966013262301163], [-0.32691133022814595, 0.498, 0.37186747562269029], [0.25533848511500978, 0.42399999999999999, 0.39923083899077472], [2.7045138116476508, 0.0050000000000000001, 0.0034202212972238577], [-0.1551614486076057, 0.44400000000000001, 0.43834701985429037], [1.9524487722567723, 0.012999999999999999, 0.025442473674991528], [-1.2055816465306763, 0.017000000000000001, 0.11398941970467646], [3.478472976017831, 0.002, 0.00025213964072468009], [-1.4621715757903719, 0.002, 0.071847099325659136], [-0.84010307600180256, 0.085000000000000006, 0.20042529779230778], [5.7097646237318243, 0.0030000000000000001, 5.6566262784940591e-09], [1.5082367956567375, 0.065000000000000002, 0.065746966514827365], [-0.58337270103430816, 0.44, 0.27982121546450034], [-0.083271860457022437, 0.45100000000000001, 0.46681768733385554], [-0.46872337815000953, 0.34599999999999997, 0.31963368715684204], [0.18490279849545319, 0.23799999999999999, 0.42665263797981101], [3.470424529947997, 0.012, 0.00025981817437825683], [-0.99942612137154796, 0.032000000000000001, 0.15879415560388499], [-1.3650387953594485, 0.034000000000000002, 0.08612042845912049], [1.8617160516432014, 0.081000000000000003, 0.03132156240215267], [1.1321188945775384, 0.11600000000000001, 0.12879222611766061], [0.064116686050580601, 0.27300000000000002, 0.4744386578180424], [-0.42032194540259099, 0.29999999999999999, 0.33712514016213468], [-0.79581215423980922, 0.123, 0.21307061309098785], [-0.42792753720906046, 0.45600000000000002, 0.33435193892883741], [-1.0629378527428395, 0.051999999999999998, 0.14390506780140866], [-0.54164761752225477, 0.33700000000000002, 0.29403064095211839], [1.0934778886820793, 0.13700000000000001, 0.13709201601893539], [-0.094068785378413719, 0.38200000000000001, 0.46252725802998929], [0.13482026574801856, 0.36799999999999999, 0.44637699118865737], [-0.13976995315653129, 0.34699999999999998, 0.44442087706276601], [-0.051047663924746682, 0.32000000000000001, 0.47964376985626245], [-0.21468297736730158, 0.41699999999999998, 0.41500724761906527], [-0.20873154637330626, 0.38800000000000001, 0.41732890604390893], [-0.32427876152583485, 0.49199999999999999, 0.37286349875557478], [-0.65254842943280977, 0.374, 0.25702372075306734], [-0.48611858196118796, 0.23300000000000001, 0.31344154643990074], [-0.14482354344529477, 0.32600000000000001, 0.44242509660469886], [-0.51052030974200002, 0.439, 0.30484349480873729], [0.56814382285283538, 0.14999999999999999, 0.28496865660103166], [0.58680919931668207, 0.161, 0.27866592887231878], [0.013390357044409013, 0.25800000000000001, 0.49465818005865647], [-0.19050728887961568, 0.41399999999999998, 0.4244558160399462], [-0.60531777422216049, 0.35199999999999998, 0.2724839368239631], [1.0899331115425805, 0.127, 0.13787130480311838], [0.17015055382651084, 0.36899999999999999, 0.43244586845546418], [-0.21738337124409801, 0.40600000000000003, 0.41395479459421991], [1.0329303331079593, 0.079000000000000001, 0.15081825117169467], [1.0218317101096221, 0.104, 0.15343027913308094]]

release/python/0.5.1/crankshaft/test/fixtures/kmeans.json

@@ -0,0 +1 @@
+[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]

release/python/0.5.1/crankshaft/test/fixtures/markov.json

@@ -0,0 +1 @@
+[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]

release/python/0.5.1/crankshaft/test/fixtures/moran.json

@@ -0,0 +1,52 @@
+[[0.9319096128346788, "HH"],
+[-1.135787401862846, "HL"],
+[0.11732030672508517, "LL"],
+[0.6152779669180425, "LL"],
+[-0.14657336660125297, "LH"],
+[0.6967858120189607, "LL"],
+[0.07949310115714454, "HH"],
+[0.4703198759258987, "HH"],
+[0.4421125200498064, "HH"],
+[0.5724288737143592, "LL"],
+[0.8970743435692062, "LL"],
+[0.18327334401918674, "LL"],
+[-0.01466729201304962, "HL"],
+[0.3481559372544409, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329988, "HH"],
+[0.4373841193538136, "HH"],
+[0.15971286468915544, "LL"],
+[1.0543588860308968, "HH"],
+[1.7372866900020818, "HH"],
+[1.091998586053999, "LL"],
+[0.1171572584252222, "HH"],
+[0.08438455015300014, "LL"],
+[0.06547094736902978, "LL"],
+[0.15482141569329985, "HH"],
+[1.1627044812890683, "HH"],
+[0.06547094736902978, "LL"],
+[0.795275137550483, "HH"],
+[0.18562939195219, "LL"],
+[0.3010757406693439, "LL"],
+[2.8205795942839376, "HH"],
+[0.11259190602909264, "LL"],
+[-0.07116352791516614, "HL"],
+[-0.09945240794119009, "LH"],
+[0.18562939195219, "LL"],
+[0.1832733440191868, "LL"],
+[-0.39054253768447705, "HL"],
+[-0.1672071289487642, "HL"],
+[0.3337669247916343, "HH"],
+[0.2584386102554792, "HH"],
+[-0.19733845476322634, "HL"],
+[-0.9379282899805409, "LH"],
+[-0.028770969951095866, "LH"],
+[0.051367269430983485, "LL"],
+[-0.2172548045913472, "LH"],
+[0.05136726943098351, "LL"],
+[0.04191046803899837, "LL"],
+[0.7482357030403517, "HH"],
+[-0.014585767863118111, "LH"],
+[0.5410013139159929, "HH"],
+[1.0223932668429925, "LL"],
+[1.4179402898927476, "LL"]]

release/python/0.5.1/crankshaft/test/fixtures/neighbors.json

@@ -0,0 +1,54 @@
+[
+    {"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
+    {"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
+    {"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
+    {"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
+    {"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
+    {"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
+    {"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
+    {"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
+    {"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
+    {"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
+    {"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
+    {"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
+    {"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
+    {"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
+    {"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
+    {"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
+    {"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
+    {"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
+    {"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
+    {"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
+    {"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
+    {"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
+    {"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
+    {"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
+    {"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
+    {"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
+    {"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
+    {"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
+    {"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
+    {"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
+    {"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
+    {"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
+    {"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
+    {"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
+    {"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
+    {"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
+    {"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
+    {"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
+    {"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
+    {"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
+    {"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
+    {"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
+    {"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
+    {"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
+    {"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
+    {"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
+    {"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
+    {"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
+    {"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
+    {"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
+    {"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
+    {"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
+  ]

release/python/0.5.1/crankshaft/test/helper.py

@@ -0,0 +1,13 @@
+import unittest
+
+from mock_plpy import MockPlPy
+plpy = MockPlPy()
+
+import sys
+sys.modules['plpy'] = plpy
+
+import os
+
+def fixture_file(name):
+    dir = os.path.dirname(os.path.realpath(__file__))
+    return os.path.join(dir, 'fixtures', name)

release/python/0.5.1/crankshaft/test/mock_plpy.py

@@ -0,0 +1,54 @@
+import re
+
+
+class MockCursor:
+    def __init__(self, data):
+        self.cursor_pos = 0
+        self.data = data
+
+    def fetch(self, batch_size):
+        batch = self.data[self.cursor_pos:self.cursor_pos + batch_size]
+        self.cursor_pos += batch_size
+        return batch
+
+
+class MockPlPy:
+    def __init__(self):
+        self._reset()
+
+    def _reset(self):
+        self.infos = []
+        self.notices = []
+        self.debugs = []
+        self.logs = []
+        self.warnings = []
+        self.errors = []
+        self.fatals = []
+        self.executes = []
+        self.results = []
+        self.prepares = []
+        self.results = []
+
+    def _define_result(self, query, result):
+        pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
+        self.results.append([pattern, result])
+
+    def notice(self, msg):
+        self.notices.append(msg)
+
+    def debug(self, msg):
+        self.notices.append(msg)
+
+    def info(self, msg):
+        self.infos.append(msg)
+
+    def cursor(self, query):
+        data = self.execute(query)
+        return MockCursor(data)
+
+    # TODO: additional arguments
+    def execute(self, query):
+        for result in self.results:
+            if result[0].match(query):
+                return result[1]
+        return []

release/python/0.5.1/crankshaft/test/test_clustering_getis.py

@@ -0,0 +1,78 @@
+import unittest
+import numpy as np
+
+from helper import fixture_file
+
+from crankshaft.clustering import Getis
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+
+# Fixture files produced as follows
+#
+# import pysal as ps
+# import numpy as np
+# import random
+#
+# # setup variables
+# f = ps.open(ps.examples.get_path("stl_hom.dbf"))
+# y = np.array(f.by_col['HR8893'])
+# w_queen = ps.queen_from_shapefile(ps.examples.get_path("stl_hom.shp"))
+#
+# out_queen = [{"id": index + 1,
+#               "neighbors": [x+1 for x in w_queen.neighbors[index]],
+#               "value": val} for index, val in enumerate(y)]
+#
+# with open('neighbors_queen_getis.json', 'w') as f:
+#     f.write(str(out_queen))
+#
+# random.seed(1234)
+# np.random.seed(1234)
+# lgstar_queen = ps.esda.getisord.G_Local(y, w_queen, star=True,
+#                                         permutations=999)
+#
+# with open('getis_queen.json', 'w') as f:
+#     f.write(str(zip(lgstar_queen.z_sim,
+#                     lgstar_queen.p_sim, lgstar_queen.p_z_sim)))
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mock_data):
+        self.mock_result = mock_data
+
+    def get_getis(self, w_type, param):
+        return self.mock_result
+
+
+class GetisTest(unittest.TestCase):
+    """Testing class for Getis-Ord's G* funtion
+       This test replicates the work done in PySAL documentation:
+          https://pysal.readthedocs.io/en/v1.11.0/users/tutorials/autocorrelation.html#local-g-and-g
+    """
+
+    def setUp(self):
+        # load raw data for analysis
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors_getis.json')).read())
+
+        # load pre-computed/known values
+        self.getis_data = json.loads(
+          open(fixture_file('getis.json')).read())
+
+    def test_getis_ord(self):
+        """Test Getis-Ord's G*"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+
+        random_seeds.set_random_seeds(1234)
+        getis = Getis(FakeDataProvider(data))
+
+        result = getis.getis_ord('subquery', 'value',
+                                 'queen', None, 999, 'the_geom',
+                                 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        expected = np.array(self.getis_data)[:, 0:2]
+        for ([res_z, res_p], [exp_z, exp_p]) in zip(result, expected):
+            self.assertAlmostEqual(res_z, exp_z, delta=1e-2)

release/python/0.5.1/crankshaft/test/test_clustering_kmeans.py

@@ -0,0 +1,56 @@
+import unittest
+import numpy as np
+
+
+# from mock_plpy import MockPlPy
+# plpy = MockPlPy()
+#
+# import sys
+# sys.modules['plpy'] = plpy
+from helper import fixture_file
+from crankshaft.clustering import Kmeans
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.clustering as cc
+
+from crankshaft import random_seeds
+import json
+from collections import OrderedDict
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mocked_result):
+        self.mocked_result = mocked_result
+
+    def get_spatial_kmeans(self, query):
+        return self.mocked_result
+
+    def get_nonspatial_kmeans(self, query, standarize):
+        return self.mocked_result
+
+
+class KMeansTest(unittest.TestCase):
+    """Testing class for k-means spatial"""
+
+    def setUp(self):
+        self.cluster_data = json.loads(
+          open(fixture_file('kmeans.json')).read())
+        self.params = {"subquery": "select * from table",
+                       "no_clusters": "10"}
+
+    def test_kmeans(self):
+        """
+        """
+        data = [{'xs': d['xs'],
+                 'ys': d['ys'],
+                 'ids': d['ids']} for d in self.cluster_data]
+
+        random_seeds.set_random_seeds(1234)
+        kmeans = Kmeans(FakeDataProvider(data))
+        clusters = kmeans.spatial('subquery', 2)
+        labels = [a[1] for a in clusters]
+        c1 = [a for a in clusters if a[1] == 0]
+        c2 = [a for a in clusters if a[1] == 1]
+
+        self.assertEqual(len(np.unique(labels)), 2)
+        self.assertEqual(len(c1), 20)
+        self.assertEqual(len(c2), 20)

release/python/0.5.1/crankshaft/test/test_clustering_moran.py

@@ -0,0 +1,112 @@
+import unittest
+import numpy as np
+
+from helper import fixture_file
+from crankshaft.clustering import Moran
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+import json
+from collections import OrderedDict
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, mock_data):
+        self.mock_result = mock_data
+
+    def get_moran(self, w_type, params):
+        return self.mock_result
+
+
+class MoranTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "attr1": "andy",
+                       "attr2": "jay_z",
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.params_markov = {"id_col": "cartodb_id",
+                              "time_cols": ["_2013_dec", "_2014_jan",
+                                            "_2014_feb"],
+                              "subquery": "SELECT * FROM a_list",
+                              "geom_col": "the_geom",
+                              "num_ngbrs": 321}
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors.json')).read())
+        self.moran_data = json.loads(
+          open(fixture_file('moran.json')).read())
+
+    def test_map_quads(self):
+        """Test map_quads"""
+        from crankshaft.clustering import map_quads
+        self.assertEqual(map_quads(1), 'HH')
+        self.assertEqual(map_quads(2), 'LH')
+        self.assertEqual(map_quads(3), 'LL')
+        self.assertEqual(map_quads(4), 'HL')
+        self.assertEqual(map_quads(33), None)
+        self.assertEqual(map_quads('andy'), None)
+
+    def test_quad_position(self):
+        """Test lisa_sig_vals"""
+        from crankshaft.clustering import quad_position
+
+        quads = np.array([1, 2, 3, 4], np.int)
+
+        ans = np.array(['HH', 'LH', 'LL', 'HL'])
+        test_ans = quad_position(quads)
+
+        self.assertTrue((test_ans == ans).all())
+
+    def test_local_stat(self):
+        """Test Moran's I local"""
+        data = [OrderedDict([('id', d['id']),
+                             ('attr1', d['value']),
+                             ('neighbors', d['neighbors'])])
+                for d in self.neighbors_data]
+
+        moran = Moran(FakeDataProvider(data))
+        random_seeds.set_random_seeds(1234)
+        result = moran.local_stat('subquery', 'value',
+                                  'knn', 5, 99, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
+            self.assertAlmostEqual(res_val, exp_val)
+            self.assertEqual(res_quad, exp_quad)
+
+    def test_moran_local_rate(self):
+        """Test Moran's I rate"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'attr2': 1,
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+
+        random_seeds.set_random_seeds(1234)
+        moran = Moran(FakeDataProvider(data))
+        result = moran.local_rate_stat('subquery', 'numerator', 'denominator',
+                                       'knn', 5, 99, 'the_geom', 'cartodb_id')
+        result = [(row[0], row[1]) for row in result]
+
+        zipped_values = zip(result, self.moran_data)
+
+        for ([res_val, res_quad], [exp_val, exp_quad]) in zipped_values:
+            self.assertAlmostEqual(res_val, exp_val)
+
+    def test_moran(self):
+        """Test Moran's I global"""
+        data = [{'id': d['id'],
+                 'attr1': d['value'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+        random_seeds.set_random_seeds(1235)
+        moran = Moran(FakeDataProvider(data))
+        result = moran.global_stat('table', 'value',
+                                   'knn', 5, 99, 'the_geom',
+                                   'cartodb_id')
+
+        result_moran = result[0][0]
+        expected_moran = np.array([row[0] for row in self.moran_data]).mean()
+        self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)

release/python/0.5.1/crankshaft/test/test_pysal_utils.py

@@ -0,0 +1,160 @@
+import unittest
+
+import crankshaft.pysal_utils as pu
+from crankshaft import random_seeds
+from collections import OrderedDict
+
+
+class PysalUtilsTest(unittest.TestCase):
+    """Testing class for utility functions related to PySAL integrations"""
+
+    def setUp(self):
+        self.params1 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("attr1", "andy"),
+                                    ("attr2", "jay_z"),
+                                    ("subquery", "SELECT * FROM a_list"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params2 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("numerator", "price"),
+                                    ("denominator", "sq_meters"),
+                                    ("subquery", "SELECT * FROM pecan"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params3 = OrderedDict([("id_col", "cartodb_id"),
+                                    ("numerator", "sq_meters"),
+                                    ("denominator", "price"),
+                                    ("subquery", "SELECT * FROM pecan"),
+                                    ("geom_col", "the_geom"),
+                                    ("num_ngbrs", 321)])
+
+        self.params_array = {"id_col": "cartodb_id",
+                             "time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
+                             "subquery": "SELECT * FROM a_list",
+                             "geom_col": "the_geom",
+                             "num_ngbrs": 321}
+
+    def test_query_attr_select(self):
+        """Test query_attr_select"""
+
+        ans1 = ("i.\"andy\"::numeric As attr1, "
+                "i.\"jay_z\"::numeric As attr2, ")
+
+        ans2 = ("i.\"price\"::numeric As attr1, "
+                "i.\"sq_meters\"::numeric As attr2, ")
+
+        ans3 = ("i.\"sq_meters\"::numeric As attr1, "
+                "i.\"price\"::numeric As attr2, ")
+
+        ans_array = ("i.\"_2013_dec\"::numeric As attr1, "
+                     "i.\"_2014_jan\"::numeric As attr2, "
+                     "i.\"_2014_feb\"::numeric As attr3, ")
+
+        self.assertEqual(pu.query_attr_select(self.params1), ans1)
+        self.assertEqual(pu.query_attr_select(self.params2), ans2)
+        self.assertEqual(pu.query_attr_select(self.params3), ans3)
+        self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
+
+    def test_query_attr_where(self):
+        """Test pu.query_attr_where"""
+
+        ans1 = ("idx_replace.\"andy\" IS NOT NULL AND "
+                "idx_replace.\"jay_z\" IS NOT NULL")
+
+        ans_array = ("idx_replace.\"_2013_dec\" IS NOT NULL AND "
+                     "idx_replace.\"_2014_jan\" IS NOT NULL AND "
+                     "idx_replace.\"_2014_feb\" IS NOT NULL")
+
+        self.assertEqual(pu.query_attr_where(self.params1), ans1)
+        self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
+
+    def test_knn(self):
+        """Test knn neighbors constructor"""
+
+        ans1 = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE " \
+                                    "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                    "j.\"andy\" IS NOT NULL AND " \
+                                    "j.\"jay_z\" IS NOT NULL " \
+                                   "ORDER BY " \
+                                    "j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                      "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        ans_array = "SELECT i.\"cartodb_id\" As id, " \
+              "i.\"_2013_dec\"::numeric As attr1, " \
+              "i.\"_2014_jan\"::numeric As attr2, " \
+              "i.\"_2014_feb\"::numeric As attr3, " \
+              "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                            "FROM (SELECT * FROM a_list) As j " \
+                            "WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                  "j.\"_2013_dec\" IS NOT NULL AND " \
+                                  "j.\"_2014_jan\" IS NOT NULL AND " \
+                                  "j.\"_2014_feb\" IS NOT NULL " \
+                            "ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
+                            "LIMIT 321)) As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"_2013_dec\" IS NOT NULL AND " \
+                    "i.\"_2014_jan\" IS NOT NULL AND " \
+                    "i.\"_2014_feb\" IS NOT NULL "\
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.knn(self.params1), ans1)
+        self.assertEqual(pu.knn(self.params_array), ans_array)
+
+    def test_queen(self):
+        """Test queen neighbors constructor"""
+
+        ans1 = "SELECT i.\"cartodb_id\" As id, " \
+                     "i.\"andy\"::numeric As attr1, " \
+                     "i.\"jay_z\"::numeric As attr2, " \
+                     "(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
+                                   "FROM (SELECT * FROM a_list) As j " \
+                                   "WHERE " \
+                                   "i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
+                                   "ST_Touches(i.\"the_geom\", " \
+                                              "j.\"the_geom\") AND " \
+                                   "j.\"andy\" IS NOT NULL AND " \
+                                   "j.\"jay_z\" IS NOT NULL)" \
+                                  ") As neighbors " \
+              "FROM (SELECT * FROM a_list) As i " \
+              "WHERE i.\"andy\" IS NOT NULL AND " \
+                    "i.\"jay_z\" IS NOT NULL " \
+              "ORDER BY i.\"cartodb_id\" ASC;"
+
+        self.assertEqual(pu.queen(self.params1), ans1)
+
+    def test_construct_neighbor_query(self):
+        """Test construct_neighbor_query"""
+
+        # Compare to raw knn query
+        self.assertEqual(pu.construct_neighbor_query('knn', self.params1),
+                         pu.knn(self.params1))
+
+    def test_get_attributes(self):
+        """Test get_attributes"""
+
+        ## need to add tests
+
+        self.assertEqual(True, True)
+
+    def test_get_weight(self):
+        """Test get_weight"""
+
+        self.assertEqual(True, True)
+
+    def test_empty_zipped_array(self):
+        """Test empty_zipped_array"""
+        ans2 = [(None, None)]
+        ans4 = [(None, None, None, None)]
+        self.assertEqual(pu.empty_zipped_array(2), ans2)
+        self.assertEqual(pu.empty_zipped_array(4), ans4)

release/python/0.5.1/crankshaft/test/test_segmentation.py

@@ -0,0 +1,64 @@
+import unittest
+import numpy as np
+from helper import plpy, fixture_file
+import crankshaft.segmentation as segmentation
+import json
+
+class SegmentationTest(unittest.TestCase):
+    """Testing class for Moran's I functions"""
+
+    def setUp(self):
+        plpy._reset()
+
+    def generate_random_data(self,n_samples,random_state,  row_type=False):
+        x1 = random_state.uniform(size=n_samples)
+        x2 = random_state.uniform(size=n_samples)
+        x3 = random_state.randint(0, 4, size=n_samples)
+
+        y = x1+x2*x2+x3
+        cartodb_id  = range(len(x1))
+
+        if row_type:
+            return [ {'features': vals} for vals in zip(x1,x2,x3)], y
+        else:
+            return  [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
+
+    def test_replace_nan_with_mean(self):
+        test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
+
+    def test_create_and_predict_segment(self):
+        n_samples = 1000
+
+        random_state_train = np.random.RandomState(13)
+        random_state_test = np.random.RandomState(134)
+        training_data = self.generate_random_data(n_samples, random_state_train)
+        test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
+
+
+        ids =  [{'cartodb_ids': range(len(test_data))}]
+        rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
+
+        plpy._define_result('select \* from  \(select \* from training\) a  limit 1',rows)
+        plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
+        plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
+        plpy._define_result('.*select \* from test.*' ,test_data)
+
+        model_parameters =  {'n_estimators': 1200,
+                             'max_depth': 3,
+                             'subsample' : 0.5,
+                             'learning_rate': 0.01,
+                             'min_samples_leaf': 1}
+
+        result = segmentation.create_and_predict_segment(
+                'select * from training',
+                'target',
+                'select * from test',
+                model_parameters)
+
+        prediction = [r[1] for r in result]
+
+        accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
+
+        self.assertEqual(len(result),len(test_data))
+        self.assertTrue( result[0][2] < 0.01)
+        self.assertTrue( accuracy < 0.5*np.mean(test_y)  )

release/python/0.5.1/crankshaft/test/test_space_time_dynamics.py

@@ -0,0 +1,349 @@
+import unittest
+import numpy as np
+
+import unittest
+
+
+from helper import fixture_file
+
+from crankshaft.space_time_dynamics import Markov
+import crankshaft.space_time_dynamics as std
+from crankshaft import random_seeds
+from crankshaft.analysis_data_provider import AnalysisDataProvider
+import json
+
+
+class FakeDataProvider(AnalysisDataProvider):
+    def __init__(self, data):
+        self.mock_result = data
+
+    def get_markov(self, w_type, params):
+        return self.mock_result
+
+
+class SpaceTimeTests(unittest.TestCase):
+    """Testing class for Markov Functions."""
+
+    def setUp(self):
+        self.params = {"id_col": "cartodb_id",
+                       "time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
+                       "subquery": "SELECT * FROM a_list",
+                       "geom_col": "the_geom",
+                       "num_ngbrs": 321}
+        self.neighbors_data = json.loads(
+          open(fixture_file('neighbors_markov.json')).read())
+        self.markov_data = json.loads(open(fixture_file('markov.json')).read())
+
+        self.time_data = np.array([i * np.ones(10, dtype=float)
+                                   for i in range(10)]).T
+
+        self.transition_matrix = np.array([
+                [[0.96341463, 0.0304878, 0.00609756, 0., 0.],
+                 [0.06040268, 0.83221477, 0.10738255, 0., 0.],
+                 [0., 0.14, 0.74, 0.12, 0.],
+                 [0., 0.03571429, 0.32142857, 0.57142857, 0.07142857],
+                 [0., 0., 0., 0.16666667, 0.83333333]],
+                [[0.79831933, 0.16806723, 0.03361345, 0., 0.],
+                 [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+                 [0.00537634, 0.06989247, 0.8655914, 0.05913978, 0.],
+                 [0., 0., 0.06372549, 0.90196078, 0.03431373],
+                 [0., 0., 0., 0.19444444, 0.80555556]],
+                [[0.84693878, 0.15306122, 0., 0., 0.],
+                 [0.08133971, 0.78947368, 0.1291866, 0., 0.],
+                 [0.00518135, 0.0984456, 0.79274611, 0.0984456, 0.00518135],
+                 [0., 0., 0.09411765, 0.87058824, 0.03529412],
+                 [0., 0., 0., 0.10204082, 0.89795918]],
+                [[0.8852459, 0.09836066, 0., 0.01639344, 0.],
+                 [0.03875969, 0.81395349, 0.13953488, 0., 0.00775194],
+                 [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+                 [0., 0.02339181, 0.12865497, 0.75438596, 0.09356725],
+                 [0., 0., 0., 0.09661836, 0.90338164]],
+                [[0.33333333, 0.66666667, 0., 0., 0.],
+                 [0.0483871, 0.77419355, 0.16129032, 0.01612903, 0.],
+                 [0.01149425, 0.16091954, 0.74712644, 0.08045977, 0.],
+                 [0., 0.01036269, 0.06217617, 0.89637306, 0.03108808],
+                 [0., 0., 0., 0.02352941, 0.97647059]]]
+                 )
+
+    def test_spatial_markov(self):
+        """Test Spatial Markov."""
+        data = [{'id': d['id'],
+                 'attr1': d['y1995'],
+                 'attr2': d['y1996'],
+                 'attr3': d['y1997'],
+                 'attr4': d['y1998'],
+                 'attr5': d['y1999'],
+                 'attr6': d['y2000'],
+                 'attr7': d['y2001'],
+                 'attr8': d['y2002'],
+                 'attr9': d['y2003'],
+                 'attr10': d['y2004'],
+                 'attr11': d['y2005'],
+                 'attr12': d['y2006'],
+                 'attr13': d['y2007'],
+                 'attr14': d['y2008'],
+                 'attr15': d['y2009'],
+                 'neighbors': d['neighbors']} for d in self.neighbors_data]
+        # print(str(data[0]))
+        markov = Markov(FakeDataProvider(data))
+        random_seeds.set_random_seeds(1234)
+
+        result = markov.spatial_trend('subquery',
+                                      ['y1995', 'y1996', 'y1997', 'y1998',
+                                       'y1999', 'y2000', 'y2001', 'y2002',
+                                       'y2003', 'y2004', 'y2005', 'y2006',
+                                       'y2007', 'y2008', 'y2009'],
+                                      5, 'knn', 5, 0, 'the_geom',
+                                      'cartodb_id')
+
+        self.assertTrue(result is not None)
+        result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
+        print result[0]
+        expected = self.markov_data
+        for ([res_trend, res_up, res_down, res_vol, res_id],
+             [exp_trend, exp_up, exp_down, exp_vol, exp_id]
+             ) in zip(result, expected):
+            self.assertAlmostEqual(res_trend, exp_trend)
+
+    def test_get_time_data(self):
+        """Test get_time_data"""
+        data = [{'attr1': d['y1995'],
+                 'attr2': d['y1996'],
+                 'attr3': d['y1997'],
+                 'attr4': d['y1998'],
+                 'attr5': d['y1999'],
+                 'attr6': d['y2000'],
+                 'attr7': d['y2001'],
+                 'attr8': d['y2002'],
+                 'attr9': d['y2003'],
+                 'attr10': d['y2004'],
+                 'attr11': d['y2005'],
+                 'attr12': d['y2006'],
+                 'attr13': d['y2007'],
+                 'attr14': d['y2008'],
+                 'attr15': d['y2009']} for d in self.neighbors_data]
+
+        result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998',
+                                          'y1999', 'y2000', 'y2001', 'y2002',
+                                          'y2003', 'y2004', 'y2005', 'y2006',
+                                          'y2007', 'y2008', 'y2009'])
+
+        # expected was prepared from PySAL example:
+        # f = ps.open(ps.examples.get_path("usjoin.csv"))
+        # pci = np.array([f.by_col[str(y)]
+        #                 for y in range(1995, 2010)]).transpose()
+        # rpci = pci / (pci.mean(axis = 0))
+
+        expected = np.array(
+          [[0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154,
+            0.83271652,  0.83786314, 0.85012593, 0.85509656, 0.86416612,
+            0.87119375, 0.86302631,  0.86148267, 0.86252252, 0.86746356],
+           [0.9188951,  0.91757931, 0.92333258, 0.92517289, 0.92552388,
+            0.90746978,  0.89830489, 0.89431991, 0.88924794, 0.89815176,
+            0.91832091, 0.91706054,  0.90139505, 0.87897455, 0.86216858],
+           [0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522,
+            0.78964559,  0.80584442, 0.8084998,  0.82258551, 0.82668196,
+            0.82373724, 0.81814804,  0.83675961, 0.83574199, 0.84647177],
+           [1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841,
+            1.14506948,  1.12151133, 1.11160697, 1.10888621, 1.11399806,
+            1.12168029, 1.13164797,  1.12958508, 1.11371818, 1.09936775],
+           [1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025,
+            1.16898201,  1.17212488, 1.14752303, 1.11843284, 1.11024964,
+            1.11943471, 1.11736468,  1.10863242, 1.09642516, 1.07762337],
+           [1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684,
+            1.44184737,  1.44782832, 1.41978227, 1.39092208, 1.4059372,
+            1.40788646, 1.44052766,  1.45241216, 1.43306098, 1.4174431],
+           [1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149,
+            1.10888138,  1.11856629, 1.13062931, 1.11944984, 1.12446239,
+            1.11671008, 1.10880034,  1.08401709, 1.06959206, 1.07875225],
+           [1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545,
+            0.99854316,  0.9880258,  0.99669587, 0.99327676, 1.01400905,
+            1.03176742, 1.040511,  1.01749645, 0.9936394,  0.98279746],
+           [0.98996986, 1.00143564, 0.99491,  1.00188408, 1.00455845,
+            0.99127006,  0.97925917, 0.9683482,  0.95335147, 0.93694787,
+            0.94308213, 0.92232874,  0.91284091, 0.89689833, 0.88928858],
+           [0.87418391, 0.86416601, 0.84425695, 0.8404494,  0.83903044,
+            0.8578708,  0.86036185, 0.86107306, 0.8500772,  0.86981998,
+            0.86837929, 0.87204141,  0.86633032, 0.84946077, 0.83287146],
+           [1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624,
+            1.14450183,  1.12349752, 1.12596664, 1.12213996, 1.1119989,
+            1.10257792, 1.10491258,  1.11059842, 1.10509795, 1.10020097],
+           [0.97282463, 0.96700147, 0.96252588, 0.9653878,  0.96057687,
+            0.95831051,  0.94480909, 0.94804195, 0.95430286, 0.94103989,
+            0.92122519, 0.91010201,  0.89280392, 0.89298243, 0.89165385],
+           [0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647,
+            0.9480927,  0.93539182, 0.95388718, 0.94597005, 0.96918424,
+            0.94781281, 0.93466815,  0.94281559, 0.96520315, 0.96715441],
+           [0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897,
+            0.98687073,  0.99237486, 0.98209969, 0.9877653,  0.97399471,
+            0.96910087, 0.98416665,  0.98423613, 0.99823861, 0.99545704],
+           [0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012,
+            0.86191535,  0.84981451, 0.85472102, 0.84564835, 0.83998883,
+            0.83478547, 0.82803648,  0.8198736,  0.82265395, 0.8399404],
+           [0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136,
+            0.82785597,  0.86008789, 0.86776298, 0.86720209, 0.8676334,
+            0.89179317, 0.94202108,  0.9422231,  0.93902708, 0.94479184],
+           [0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238,
+            0.90906632,  0.92693339, 0.93695966, 0.94242697, 0.94338265,
+            0.91981796, 0.91108804,  0.90543476, 0.91737138, 0.94793657],
+           [1.1977611,  1.18222564, 1.18439158, 1.18267865, 1.19286723,
+            1.20172869,  1.21328691, 1.22624778, 1.22397075, 1.23857042,
+            1.24419893, 1.23929384,  1.23418676, 1.23626739, 1.26754398],
+           [1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667,
+            1.34790023,  1.34399863, 1.32575181, 1.30795492, 1.30544841,
+            1.30303302, 1.32107766,  1.32936244, 1.33001241, 1.33288462],
+           [1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093,
+            1.05059016,  1.03405057, 1.02747623, 1.03162734, 0.9961416,
+            0.97356208, 0.94241549,  0.92754547, 0.92549227, 0.92138102],
+           [1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264,
+            1.13889622,  1.12442212, 1.13367018, 1.13982256, 1.14029944,
+            1.11979401, 1.10905389,  1.10577769, 1.11166825, 1.09985155],
+           [0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284,
+            0.74480073,  0.76098396, 0.76156903, 0.76651952, 0.76533288,
+            0.78205934, 0.76842416,  0.77487118, 0.77768683, 0.78801192],
+           [0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803,
+            0.97370819,  0.96419154, 0.97209861, 0.97441313, 0.96356162,
+            0.94745352, 0.93965462,  0.93069645, 0.94020973, 0.94358232],
+           [0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801,
+            0.80071489,  0.83358256, 0.83451613, 0.85175032, 0.85954307,
+            0.86790024, 0.87170334,  0.87863799, 0.87497981, 0.87888675],
+           [0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619,
+            0.98733195,  0.99644997, 0.99669587, 1.02559097, 1.01116651,
+            0.99988024, 0.97906749,  0.99323123, 1.00204939, 0.99602148],
+           [1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683,
+            1.08312397,  1.05192626, 1.04230892, 1.05577278, 1.08569751,
+            1.12443486, 1.08891079,  1.08603695, 1.05997314, 1.02160943],
+           [1.11368269, 1.1057147,  1.11893431, 1.13778669, 1.1432272,
+            1.18257029,  1.16226243, 1.16009196, 1.14467789, 1.14820235,
+            1.12386598, 1.12680236,  1.12357937, 1.1159258,  1.12570828],
+           [1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667,
+            1.31210239,  1.29989156, 1.29203193, 1.27183516, 1.26830786,
+            1.2617743,  1.28656675,  1.29734097, 1.29390205, 1.29345446],
+           [0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864,
+            0.78772975,  0.82848011, 0.8259679,  0.82435705, 0.83108634,
+            0.84373784, 0.83891093,  0.84349247, 0.85637272, 0.86539395],
+           [1.23450087, 1.2426022,  1.23537935, 1.23581293, 1.24522626,
+            1.2256767,  1.21126648, 1.19377804, 1.18355337, 1.19674434,
+            1.21536573, 1.23653297,  1.27962009, 1.27968392, 1.25907738],
+           [0.9769662,  0.97400719, 0.98035944, 0.97581531, 0.95543282,
+            0.96480308,  0.94686376, 0.93679073, 0.92540049, 0.92988835,
+            0.93442917, 0.92100464,  0.91475304, 0.90249622, 0.9021363],
+           [0.84986886, 0.8986851,  0.84295997, 0.87280534, 0.85659368,
+            0.88937573,  0.894401, 0.90448993, 0.95495898, 0.92698333,
+            0.94745352, 0.92562488,  0.96635366, 1.02520312, 1.0394296],
+           [1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073,
+            1.00759019,  0.99192968, 0.99747298, 0.99550759, 0.97583768,
+            0.9610168,  0.94779638,  0.93759089, 0.93353431, 0.94121705],
+           [0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613,
+            0.83434854,  0.85813595, 0.84667961, 0.84374558, 0.85951183,
+            0.87194227, 0.89455097,  0.88283929, 0.90349491, 0.90600675],
+           [1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086,
+            1.00581626,  0.98850522, 0.99291168, 0.98983209, 0.97511924,
+            0.96134615, 0.96382634,  0.95011401, 0.9434686,  0.94637765],
+           [1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857,
+            1.04800023,  1.03024941, 1.04200483, 1.0402554,  1.03296979,
+            1.02191682, 1.02476275,  1.02347523, 1.02517684, 1.04359571],
+           [1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043,
+            1.0531801,  1.07452771, 1.09383478, 1.1052447,  1.10322136,
+            1.09167939, 1.08772756,  1.08859544, 1.09177338, 1.1096083],
+           [0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809,
+            0.86287327,  0.85169796, 0.85411285, 0.84886336, 0.84517414,
+            0.84843858, 0.84488343,  0.83374329, 0.82812044, 0.82878599],
+           [0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286,
+            0.92652175,  0.94278865, 0.93682452, 0.98655146, 0.992237,
+            0.9798497,  0.93869677,  0.96947771, 1.00362626, 0.98102351],
+           [0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967,
+            0.93092109,  0.92662519, 0.93412152, 0.93501274, 0.92879506,
+            0.92110542, 0.91035556,  0.90430364, 0.89994694, 0.90073864],
+           [0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824,
+            0.98882205,  0.97662234, 0.95601578, 0.94905385, 0.94934888,
+            0.97152609, 0.97163004,  0.9700702,  0.97158948, 0.95884908],
+           [0.83980439, 0.84726737, 0.85747,  0.85467221, 0.8556751,
+            0.84818516,  0.85265681, 0.84502402, 0.82645665, 0.81743586,
+            0.83550406, 0.83338919,  0.83511679, 0.82136617, 0.80921874],
+           [0.95118156, 0.9466212,  0.94688098, 0.9508583,  0.9512441,
+            0.95440787,  0.96364363, 0.96804412, 0.97136214, 0.97583768,
+            0.95571724, 0.96895368,  0.97001634, 0.97082733, 0.98782366],
+           [1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249,
+            1.10558188,  1.1214086,  1.12292577, 1.13021031, 1.13342735,
+            1.14686068, 1.14502975,  1.14474747, 1.14084037, 1.16142926],
+           [1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863,
+            1.11856702,  1.09764283, 1.08815849, 1.08044313, 1.09278827,
+            1.07003204, 1.08398066,  1.09831768, 1.09298232, 1.09176125],
+           [0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744,
+            0.77751194,  0.79902974, 0.81437881, 0.80788828, 0.79603865,
+            0.78966436, 0.79949807,  0.80172182, 0.82168155, 0.85587911],
+           [1.0052447,  1.00007696, 1.00475899, 1.00613942, 1.00639561,
+            1.00162979,  0.99860739, 1.00814981, 1.00574316, 0.99030032,
+            0.97682565, 0.97292596,  0.96519561, 0.96173403, 0.95890284],
+           [0.95808419, 0.9382568,  0.9654441,  0.95561201, 0.96987289,
+            0.96608031,  0.99727185, 1.00781194, 1.03484236, 1.05333619,
+            1.0983263,  1.1704974,  1.17025154, 1.18730553, 1.14242645]])
+
+        self.assertTrue(np.allclose(result, expected))
+        self.assertTrue(type(result) == type(expected))
+        self.assertTrue(result.shape == expected.shape)
+
+    def test_rebin_data(self):
+        """Test rebin_data"""
+        # sample in double the time (even case since 10 % 2 = 0):
+        #   (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
+        # = 0.5,     2.5,     4.5,     6.5,     8.5
+        ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
+                             for i in range(0, 10, 2)]).T
+
+        self.assertTrue(
+          np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
+
+        # sample in triple the time (uneven since 10 % 3 = 1):
+        #   (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
+        # = 1,         4,         7,         9
+        ans_odd = np.array([i * np.ones(10, dtype=float)
+                            for i in (1, 4, 7, 9)]).T
+        self.assertTrue(
+          np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
+
+    def test_get_prob_dist(self):
+        """Test get_prob_dist"""
+        lag_indices = np.array([1, 2, 3, 4])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer = np.array([
+            [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+            [0., 0., 0.09411765, 0.87058824, 0.03529412],
+            [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+            [0., 0., 0., 0.02352941, 0.97647059]
+        ])
+        result = std.get_prob_dist(self.transition_matrix,
+                                   lag_indices, unit_indices)
+
+        self.assertTrue(np.array_equal(result, answer))
+
+    def test_get_prob_stats(self):
+        """Test get_prob_stats"""
+
+        probs = np.array([
+            [0.0754717, 0.88207547, 0.04245283, 0., 0.],
+            [0., 0., 0.09411765, 0.87058824, 0.03529412],
+            [0.0049505, 0.09405941, 0.77722772, 0.11881188, 0.0049505],
+            [0., 0., 0., 0.02352941, 0.97647059]
+        ])
+        unit_indices = np.array([1, 3, 2, 4])
+        answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
+        answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
+        answer_trend = np.array([-0.03301887 / 0.88207547,
+                                 -0.05882353 / 0.87058824,
+                                 0.02475248 / 0.77722772,
+                                 -0.02352941 / 0.97647059])
+        answer_volatility = np.array([0.34221495,  0.33705421,
+                                      0.29226542,  0.38834223])
+
+        result = std.get_prob_stats(probs, unit_indices)
+        result_up = result[0]
+        result_down = result[1]
+        result_trend = result[2]
+        result_volatility = result[3]
+
+        self.assertTrue(np.allclose(result_up, answer_up))
+        self.assertTrue(np.allclose(result_down, answer_down))
+        self.assertTrue(np.allclose(result_trend, answer_trend))
+        self.assertTrue(np.allclose(result_volatility, answer_volatility))

src/pg/crankshaft.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Spatial Analysis extension'
-default_version = '0.5.0'
+default_version = '0.5.1'
 requires = 'plpythonu, postgis'
 superuser = true
 schema = cdb_crankshaft