Walmart Recruiting data added

Author: Rupak Chakraborty

Walmart Recruiting - Sales in Stormy Weather/Data-Walmart Recruiting II_ Sales in Stormy Weather _ Kaggle.pdf

@@ -0,0 +1,131 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import BaggingRegressor
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomTreesEmbedding
+from sklearn.neural_network import MLPRegressor
+from sklearn.linear_model import ElasticNet
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import Imputer
+from sklearn import metrics
+import numpy as np
+
+
+def get_gaussian_process_regressor():
+    gp = GaussianProcessRegressor()
+    return [gp],['Gaussian Process']
+
+
+def get_mlp_regressor(num_hidden_units=51):
+    mlp = MLPRegressor(hidden_layer_sizes=num_hidden_units)
+    return [mlp],['Multi-Layer Perceptron']
+
+
+def get_ensemble_models():
+    rf = RandomForestRegressor(n_estimators=51,min_samples_leaf=5,min_samples_split=3,random_state=42)
+    bag = BaggingRegressor(n_estimators=51,random_state=42)
+    extra = ExtraTreesRegressor(n_estimators=71,random_state=42)
+    ada = AdaBoostRegressor(random_state=42)
+    grad = GradientBoostingRegressor(n_estimators=101,random_state=42)
+    classifier_list = [rf,bag,extra,ada,grad]
+    classifier_name_list = ['Random Forests','Bagging','Extra Trees','AdaBoost','Gradient Boost']
+    return classifier_list, classifier_name_list
+
+
+def get_linear_model():
+    elastic_net = ElasticNet()
+    return [elastic_net],['Elastic Net']
+
+
+def print_evaluation_metrics(trained_model,trained_model_name,X_test,y_test):
+    print '--------- For Model : ', trained_model_name ,' ---------\n'
+    predicted_values = trained_model.predict(X_test)
+    print "Mean Absolute Error : ", metrics.mean_absolute_error(y_test,predicted_values)
+    print "Median Absolute Error : ", metrics.median_absolute_error(y_test,predicted_values)
+    print "Mean Squared Error : ", metrics.mean_squared_error(y_test,predicted_values)
+    print "R2 Score : ", metrics.r2_score(y_test,predicted_values)
+    print "---------------------------------------\n"
+
+
+def label_encode_frame(dataframe):
+    columns = dataframe.columns
+    encoder = LabelEncoder()
+    for column in columns:
+        if type(dataframe[column][0]) is np.nan:
+            for i in range(len(dataframe)):
+                if i > 1000:
+                    break
+                if type(dataframe[column][i]) is str:
+                    dataframe[column] = encoder.fit_transform(dataframe[column].values)
+                    break
+        elif type(dataframe[column][0]) is str:
+            dataframe[column] = encoder.fit_transform(dataframe[column].values)
+    return dataframe
+
+
+def spilt_date(list_of_date_string,separator='-',format='yyyy-mm-dd'):
+    month_list = list([])
+    day_list = list([])
+    year_list = list([])
+    for date_string in list_of_date_string:
+        date_list = date_string.strip().split(separator)
+        month_list.append(date_list[1])
+        day_list.append(date_list[2])
+        year_list.append(date_list[0])
+    return month_list,day_list,year_list
+
+
+def isfloat(value):
+    try:
+        float(value)
+        return True
+    except:
+        return False
+
+
+def handle_mixed_data_types(dataframe):
+    for column_name in dataframe.columns:
+        column_data = list(dataframe[column_name].values)
+        float_count = 0
+        float_sum = 0.0
+        string_count = 0
+        for data in column_data:
+            if isfloat(data):
+                float_count += 1.0
+                float_sum += float(data)
+            else:
+                string_count += 1
+        if float_count >= string_count:
+            mean = float_sum/float_count
+            for index,value in enumerate(column_data):
+                if not isfloat(value):
+                    column_data[index] = mean
+                else:
+                    column_data[index] = float(value)
+        dataframe[column_name] = column_data
+    return dataframe
+
+
+weather_filename = 'weather.csv'
+train_filename = 'train.csv'
+key_filename = 'key.csv'
+weather_frame = pd.read_csv(weather_filename)
+train_frame = pd.read_csv(train_filename)
+key_frame = pd.read_csv(key_filename)
+weather_frame.drop(['codesum','depart'],axis=1,inplace=True)
+weather_frame = handle_mixed_data_types(weather_frame)
+final_frame = pd.merge(train_frame,key_frame,how='inner',left_on='store_nbr',right_on='store_nbr')
+final_frame = pd.merge(final_frame,weather_frame,how='inner',left_on=['station_nbr','date'],right_on=['station_nbr','date'])
+target_values = list(final_frame['units'].values)
+final_frame['month'], final_frame['day'], final_frame['year'] = spilt_date(list(final_frame['date'].values))
+del final_frame['units']
+del final_frame['date']
+X_train,X_test,y_train,y_test = train_test_split(final_frame.values,target_values,test_size=0.2,random_state=42)
+regressor_list,regressor_name_list = get_ensemble_models()
+for regressor,regressor_name in zip(regressor_list,regressor_name_list):
+    regressor.fit(X_train,y_train)
+    print_evaluation_metrics(regressor,regressor_name,X_test,y_test)

Walmart Recruiting - Sales in Stormy Weather/Walmart Recruiting II_ Sales in Stormy Weather _ Kaggle.pdf

@@ -0,0 +1,101 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import BaggingRegressor
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomTreesEmbedding
+from sklearn.neural_network import MLPRegressor
+from sklearn.linear_model import ElasticNet
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import Imputer
+from sklearn import metrics
+import numpy as np
+
+
+def get_gaussian_process_regressor():
+    gp = GaussianProcessRegressor()
+    return [gp],['Gaussian Process']
+
+
+def get_mlp_regressor(num_hidden_units=51):
+    mlp = MLPRegressor(hidden_layer_sizes=num_hidden_units)
+    return [mlp],['Multi-Layer Perceptron']
+
+
+def get_ensemble_models():
+    rf = RandomForestRegressor(n_estimators=51,min_samples_leaf=5,min_samples_split=3,random_state=42)
+    bag = BaggingRegressor(n_estimators=51,random_state=42)
+    extra = ExtraTreesRegressor(n_estimators=71,random_state=42)
+    ada = AdaBoostRegressor(random_state=42)
+    grad = GradientBoostingRegressor(n_estimators=101,random_state=42)
+    classifier_list = [rf,bag,extra,ada,grad]
+    classifier_name_list = ['Random Forests','Bagging','Extra Trees','AdaBoost','Gradient Boost']
+    return classifier_list, classifier_name_list
+
+
+def get_linear_model():
+    elastic_net = ElasticNet()
+    return [elastic_net],['Elastic Net']
+
+
+def print_evaluation_metrics(trained_model,trained_model_name,X_test,y_test):
+    print '--------- For Model : ', trained_model_name ,' ---------\n'
+    predicted_values = trained_model.predict(X_test)
+    print "Mean Absolute Error : ", metrics.mean_absolute_error(y_test,predicted_values)
+    print "Median Absolute Error : ", metrics.median_absolute_error(y_test,predicted_values)
+    print "Mean Squared Error : ", metrics.mean_squared_error(y_test,predicted_values)
+    print "R2 Score : ", metrics.r2_score(y_test,predicted_values)
+    print "---------------------------------------\n"
+
+
+def label_encode_frame(dataframe):
+    columns = dataframe.columns
+    encoder = LabelEncoder()
+    for column in columns:
+        if type(dataframe[column][0]) is np.nan:
+            for i in range(len(dataframe)):
+                if i > 1000:
+                    break
+                if type(dataframe[column][i]) is str or type(dataframe[column][i]) is bool:
+                    dataframe[column] = encoder.fit_transform(dataframe[column].values)
+                    break
+        elif type(dataframe[column][0]) is str or type(dataframe[column][0]) is bool:
+            dataframe[column] = encoder.fit_transform(dataframe[column].values)
+    return dataframe
+
+
+def spilt_date(list_of_date_string,separator='-',format='yyyy-mm-dd'):
+    month_list = list([])
+    day_list = list([])
+    year_list = list([])
+    for date_string in list_of_date_string:
+        date_list = date_string.strip().split(separator)
+        month_list.append(int(date_list[1]))
+        day_list.append(int(date_list[2]))
+        year_list.append(int(date_list[0]))
+    return month_list,day_list,year_list
+
+
+train_filename = 'train.csv'
+feature_filename = 'features.csv'
+train_frame = pd.read_csv(train_filename)
+feature_frame = pd.read_csv(feature_filename)
+columns_to_delete = ['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5','Date','IsHoliday']
+feature_frame.drop(columns_to_delete,axis=1,inplace=True)
+merged_frame = pd.merge(train_frame,feature_frame,left_on='Store',right_on='Store')
+target_values = list(merged_frame['Weekly_Sales'].values)
+merged_frame['Month'], merged_frame['Day'], merged_frame['Year'] = spilt_date(list(merged_frame['Date'].values))
+del merged_frame['Date']
+del merged_frame['Weekly_Sales']
+merged_frame = label_encode_frame(merged_frame)
+print len(merged_frame)
+X_train,X_test,y_train,y_test = train_test_split(merged_frame.values,target_values,test_size=0.2,random_state=42)
+regressor_list,regressor_name_list = get_ensemble_models()
+for regressor,regressor_name in zip(regressor_list,regressor_name_list):
+    regressor.fit(X_train,y_train)
+    print_evaluation_metrics(regressor,regressor_name,X_test,y_test)
+
+