fleshing out segmentation
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Stuart Lynn
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746dcc9723
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from segmentation import *
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"""
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Segmentation creation and prediction
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"""
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import sklearn
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import numpy as np
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import pandas as pd
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import pickle
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import plpy
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from sklearn.ensemble import ExtraTreesRegressor
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from sklearn import metrics
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from sklearn.cross_validation import train_test_split
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# High level interface ---------------------------------------
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def cdb_create_segment(segment_name,table_name,column_name,geoid_column,census_table,method):
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"""
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generate a segment with machine learning
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Stuart Lynn
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"""
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data = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table))
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features = data[data.columns.difference([column_name, 'geoid'])]
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target, mean, std = normalize(data[column_name])
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model, accuracy = train_model(target,features, test_split=0.2)
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save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
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return accuracy
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def normalize(target):
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mean = np.mean(target)
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std = no.std(target)
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return (target - mean)/std, mean, std
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def denormalize(target, mean ,std):
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return target*std + mean
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def train_model(target,features,test_split):
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features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
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model = ExtraTreesRegressor(n_estimators = 40, max_features=len(features.columns))
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model.fit(features_train, target_train)
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accuracy = calculate_model_accuracy(model,features,target)
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return model, accuracy
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def calculate_model_accuracy(model,features,target):
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prediction = self.model.predict(features)
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return metrics.mean_squared_error(prediction,target)/np.std(target)
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def join_with_census(table_name, column_name, geoid_column, census_table):
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coulmns = plpy.execute('select {census_table}.* limit 1 ')
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feature_names = ",".join(columns.keys.difference(['the_geom','cartodb_id']))
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join_data = plpy.execute('''
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WITH region_extent AS (
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SELECT ST_Extent(the_geom) as table_extent FROM {table_name};
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)
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SELECT {features_names}, {table_name}.{column_name}
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FROM {table_name} ,region_extent
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JOIN {census_table}
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ON {table_name}.{geoid_column} = {census_table}.geoid
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WHERE {census_table}.the_geom && region_extent.table_extent
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'''.format(**locals()))
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if len(join_data) == 0:
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plpy.notice('Failed to join with census data')
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return join_data
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def cdb_predict_segment(segment_name,geoid_column,census_table):
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"""
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predict a segment with machine learning
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Stuart Lynn
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"""
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data = fetch_model(segment_name)
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model = data['model']
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features = ",".join(data['features'])
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targets = plpy.execute('select {features} from {census_table}')
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geo_ids = plpy.execute('select geoid from {census_table}')
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result = model.predict(targets)
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return zip(geo_ids,prediction)
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def fetch_model(model_name):
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"""
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fetch a model from storage
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"""
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data = plpy.execute('select * from models where name={model_name}')
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if len(data)==0:
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plpy.notice('model not found')
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data = data[0]
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data['model'] = pickle.load(data['model'])
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return data
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def create_model_table(model_name):
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"""
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create the model table if requred
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"""
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plpy.execute('''
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CREATE table IF NOT EXISTS _cdb_models(
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name TEXT,
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model BLOB,
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features TEXT[],
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accuracy NUMERIC,
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table_name TEXT,
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)''')
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def save_model(model_name,model,accuracy,table_name, column_name,census_table,geoid_column,method):
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"""
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save a model to the model table for later use
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"""
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plpy.execute('''
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DELETE FROM _cdb_models WHERE model_name = {model_name}
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'''.format(**locals()))
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plpy.execute("""
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INSERT INTO _cdb_models ({model_name},{model_pickle},{accuracy})
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""")
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def
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# """
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# Segmentation creation and prediction
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# """
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#
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# import sklearn
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# import numpy as np
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# import pandas as pd
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# import pickle
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# import plpy
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# from sklearn.ensemble import ExtraTreesRegressor
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# from sklearn import metrics
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# from sklearn.cross_validation import train_test_split
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#
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# # High level interface ---------------------------------------
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#
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# def cdb_create_segment(segment_name,table_name,column_name,geoid_column,census_table,method):
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# """
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# generate a segment with machine learning
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# Stuart Lynn
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# """
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# data = pd.DataFrame(join_with_census(table_name, column_name,geoid_column, census_table))
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# features = data[data.columns.difference([column_name, 'geoid'])]
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# target, mean, std = normalize(data[column_name])
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# model, accuracy = train_model(target,features, test_split=0.2)
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# save_model(segment_name, model, accuracy, table_name, column_name, census_table, geoid_column, method)
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# return accuracy
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#
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# def normalize(target):
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# mean = np.mean(target)
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# std = no.std(target)
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# return (target - mean)/std, mean, std
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#
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# def denormalize(target, mean ,std):
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# return target*std + mean
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#
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# def train_model(target,features,test_split):
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# features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
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# model = ExtraTreesRegressor(n_estimators = 40, max_features=len(features.columns))
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# model.fit(features_train, target_train)
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# accuracy = calculate_model_accuracy(model,features,target)
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# return model, accuracy
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#
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# def calculate_model_accuracy(model,features,target):
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# prediction = self.model.predict(features)
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# return metrics.mean_squared_error(prediction,target)/np.std(target)
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#
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# def join_with_census(table_name, column_name, geoid_column, census_table):
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# coulmns = plpy.execute('select {census_table}.* limit 1 ')
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# feature_names = ",".join(columns.keys.difference(['the_geom','cartodb_id']))
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# join_data = plpy.execute('''
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# WITH region_extent AS (
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# SELECT ST_Extent(the_geom) as table_extent FROM {table_name};
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# )
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# SELECT {features_names}, {table_name}.{column_name}
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# FROM {table_name} ,region_extent
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# JOIN {census_table}
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# ON {table_name}.{geoid_column} = {census_table}.geoid
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# WHERE {census_table}.the_geom && region_extent.table_extent
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# '''.format(**locals()))
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#
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# if len(join_data) == 0:
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# plpy.notice('Failed to join with census data')
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#
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# return join_data
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#
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# def cdb_predict_segment(segment_name,geoid_column,census_table):
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# """
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# predict a segment with machine learning
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# Stuart Lynn
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# """
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# data = fetch_model(segment_name)
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# model = data['model']
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# features = ",".join(data['features'])
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# targets = plpy.execute('select {features} from {census_table}')
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# geo_ids = plpy.execute('select geoid from {census_table}')
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# result = model.predict(targets)
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# return zip(geo_ids,prediction)
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#
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#
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# def fetch_model(model_name):
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# """
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# fetch a model from storage
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# """
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# data = plpy.execute('select * from models where name={model_name}')
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# if len(data)==0:
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# plpy.notice('model not found')
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# data = data[0]
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# data['model'] = pickle.load(data['model'])
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# return data
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#
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#
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# def create_model_table(model_name):
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# """
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# create the model table if requred
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# """
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# plpy.execute('''
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# CREATE table IF NOT EXISTS _cdb_models(
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# name TEXT,
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# model BLOB,
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# features TEXT[],
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# accuracy NUMERIC,
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# table_name TEXT,
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# )''')
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#
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# def save_model(model_name,model,accuracy,table_name, column_name,census_table,geoid_column,method):
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# """
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# save a model to the model table for later use
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# """
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#
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# plpy.execute('''
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# DELETE FROM _cdb_models WHERE model_name = {model_name}
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# '''.format(**locals()))
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#
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# plpy.execute("""
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# INSERT INTO _cdb_models ({model_name},{model_pickle},{accuracy})
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# """)
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#
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# def
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