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WagglePOC.py
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# Databricks notebook source
df = spark.read.format("bigquery").option("viewsEnabled","true").option("materializationDataset","spark_materialization").option("materializationProject","wmt-fin-fcp-uat-ds").option("project","wmt-fin-fcp-dev").option("parentProject","wmt-fin-fcp-dev").option('table',str("wmt_us_fcp_trc_pnl.Waggle_POC1")).load()
df.registerTempTable("Waggle_POC")
data=sqlContext.sql("select DISTINCT FISCAL_YR_NBR,FISCAL_QTR_NBR, WM_WK_NBR,COUNT(*) as NUMRECS from Waggle_POC GROUP BY FISCAL_YR_NBR,FISCAL_QTR_NBR,WM_WK_NBR ORDER BY FISCAL_YR_NBR,FISCAL_QTR_NBR,WM_WK_NBR")
# display(data)
##CANADA
df = spark.read.format("bigquery").option("viewsEnabled","true").option("materializationDataset","spark_materialization").option("materializationProject","wmt-fin-fcp-uat-ds").option("project","wmt-fin-fcp-dev").option("parentProject","wmt-fin-fcp-dev").option('table',str("wmt_us_fcp_trc_pnl.Waggle_CAN_POC1")).load()
df.registerTempTable("Waggle_CAN_POC")
# COMMAND ----------
## CANADA
data = data=sqlContext.sql("SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR, DEPT_NBR,CAN_AMT as AMT, PA_NUS_PRVT_PP,PA_NUS_PPPC_RF,PA_NUS_PPP,PA_NUS_ATLS,NY_TTF_GNFS_KN,NY_TRF_NCTR_CN,NY_TRF_NCTR_CD,NY_GSR_NFCY_KN,NY_GSR_NFCY_CN,NY_GSR_NFCY_CD,NY_GNS_ICTR_ZS,NY_GNS_ICTR_GN_ZS,NY_GNS_ICTR_CN,NY_GNS_ICTR_CD,NY_GNP_PCAP_PP_KD,NY_GNP_PCAP_PP_CD,NY_GNP_PCAP_KN,NY_GNP_PCAP_KD_ZG FROM Waggle_CAN_POC WHERE CAN_AMT>0 AND WM_WK_NBR=35")
# data = data=sqlContext.sql("SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR, DEPT_NBR,CAN_AMT as AMT, PA_NUS_PRVT_PP,PA_NUS_PPPC_RF,PA_NUS_PPP,PA_NUS_ATLS,NY_TTF_GNFS_KN,NY_TRF_NCTR_CN,NY_TRF_NCTR_CD,NY_GSR_NFCY_KN,NY_GSR_NFCY_CN,NY_GSR_NFCY_CD,NY_GNS_ICTR_ZS,NY_GNS_ICTR_GN_ZS,NY_GNS_ICTR_CN,NY_GNS_ICTR_CD,NY_GNP_PCAP_PP_KD,NY_GNP_PCAP_PP_CD,NY_GNP_PCAP_KN,NY_GNP_PCAP_KD_ZG FROM Waggle_CAN_POC WHERE CAN_AMT>0 AND FISCAL_QTR_NBR=4 AND DEPT_NBR=42")
# COMMAND ----------
####** PRODUCE, DAIRY, & GROCERY @WM_WK Level**####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR IN (81,90,91,92,93,94,97,98) AND WM_WK_NBR=35")
# ####** NON PRODUCE, DAIRY, & GROCERY @WM_WK Level**####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR NOT IN (81,90,91,92,93,94,97,98,42,65) AND WM_WK_NBR=35")
# ####** GAS @WM_WK Level**#### NO MUCH DATA
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR =42 AND WM_WK_NBR=35")
# COMMAND ----------
# ####** PRODUCE, DAIRY, & GROCERY @WM_MNTH Level**####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR IN (81,90,91,92,93,94,97,98) AND FISCAL_PERIOD_NBR=9")
# ####** NON PRODUCE, DAIRY, & GROCERY @WM_MNTH Level****####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR NOT IN (81,90,91,92,93,94,97,98,42,65) AND FISCAL_PERIOD_NBR=9")
# ####** GAS @WM_MNTH Level**####NO MUCH DATA
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR =42 AND FISCAL_PERIOD_NBR=9")
# COMMAND ----------
####** PRODUCE, DAIRY, & GROCERY @WM_QTR Level**####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR IN (81,90,91,92,93,94,97,98) AND FISCAL_QTR_NBR=4")
# ####** NON PRODUCE, DAIRY, & GROCERY @WM_QTR Level****####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR NOT IN (81,90,91,92,93,94,97,98,42,65) AND FISCAL_QTR_NBR=4")
# ####** GAS @WM_QTR Level**####
# data = sqlContext.sql(" SELECT FISCAL_YR_NBR,FISCAL_QTR_NBR,FISCAL_PERIOD_NBR,WM_WK_NBR,REGION_NBR,STORE_NBR,DEPT_NBR,ACCTG_DEPT_NBR,USD_AMT as AMT,NY_GDP_DEFL_KD_ZG, NY_GDP_MKTP_KD_ZG, NY_ADJ_NNTY_PC_CD, NY_TAX_NIND_CD, NY_GNS_ICTR_CD, NY_GNP_PCAP_PP_CD, NY_GNP_MKTP_PP_CD, NY_GDS_TOTL_CD, NY_ADJ_ICTR_GN_ZS, NY_GDP_DISC_CN from Waggle_POC WHERE DEPT_NBR =42 AND FISCAL_QTR_NBR=4")
# COMMAND ----------
display(data)
# COMMAND ----------
pdf = data.toPandas()
pdf.info()
# pdf.describe()
# pdf.columns
# COMMAND ----------
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style("whitegrid")
# plt.style.use("fivethirtyeight")
# sns.pairplot(pdf)
# plt.style.use(['science','notebook','grid'])
# COMMAND ----------
pdf.apply(lambda x: pd.unique(x).tolist())
# COMMAND ----------
# pdf.corr()
# COMMAND ----------
plt.figure(figsize=(20,10))
sns.heatmap(pdf.corr(), annot=True)
# COMMAND ----------
# pdf.replace([np.inf, -np.inf], np.nan, inplace=True)
pdf.fillna(0, inplace=True)
pdf
# COMMAND ----------
# X=pdf[['FISCAL_QTR_NBR','FISCAL_PERIOD_NBR','WM_WK_NBR','REGION_NBR','STORE_NBR','DEPT_NBR','NY_GDP_DEFL_KD_ZG','NY_GDP_MKTP_KD_ZG','NY_ADJ_NNTY_PC_CD','NY_GNP_PCAP_PP_CD','NY_GNP_MKTP_PP_CD','NY_ADJ_ICTR_GN_ZS']]
# y=pdf['AMT']
# CANADA
X=pdf[['FISCAL_YR_NBR','FISCAL_QTR_NBR','FISCAL_PERIOD_NBR','WM_WK_NBR','DEPT_NBR','PA_NUS_PRVT_PP','PA_NUS_PPPC_RF','PA_NUS_PPP','PA_NUS_ATLS','NY_TTF_GNFS_KN','NY_TRF_NCTR_CN','NY_TRF_NCTR_CD','NY_GSR_NFCY_KN','NY_GSR_NFCY_CN','NY_GSR_NFCY_CD','NY_GNS_ICTR_ZS','NY_GNS_ICTR_GN_ZS','NY_GNS_ICTR_CN','NY_GNS_ICTR_CD','NY_GNP_PCAP_PP_KD','NY_GNP_PCAP_PP_CD','NY_GNP_PCAP_KN','NY_GNP_PCAP_KD_ZG']]
y=pdf['AMT']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) #, random_state=12345)
# COMMAND ----------
def correlation(dt,th):
col_corr = set()
corr_mtrx = pdf.corr()
for i in range(len(corr_mtrx.columns)):
for j in range(i):
if abs(corr_mtrx.iloc[i,j]) > th:
colnm = corr_mtrx.columns[i]
col_corr.add(colnm)
return col_corr
corr_features = correlation(pdf,0.8)
corr_features
# COMMAND ----------
from sklearn import metrics
from sklearn.model_selection import cross_val_score
def cross_val(model):
pred = cross_val_score(model, X, y, cv=10, scoring='neg_mean_squared_error')
return np.abs(pred.mean())
def print_evaluate(true, predicted):
mae = metrics.mean_absolute_error(true, predicted)
mse = metrics.mean_squared_error(true, predicted)
rmse = np.sqrt(metrics.mean_squared_error(true, predicted))
r2_square = metrics.r2_score(true, predicted)
print('MAE:', mae)
print('MSE:', mse)
print('RMSE:', rmse)
print('R2 Square', r2_square)
print('__________________________________')
def evaluate(true, predicted):
mae = metrics.mean_absolute_error(true, predicted)
mse = metrics.mean_squared_error(true, predicted)
rmse = np.sqrt(metrics.mean_squared_error(true, predicted))
r2_square = metrics.r2_score(true, predicted)
return mae, mse, rmse, r2_square
# COMMAND ----------
print("**** Linear Regression Model****")
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression(normalize=True)
lin_reg.fit(X_train,y_train)
print(lin_reg.intercept_)
coeff_df = pd.DataFrame(lin_reg.coef_, X.columns, columns=['Coefficient'])
coeff_df1 = pd.Series(lin_reg.coef_, X.columns).sort_values()
coeff_df.plot(kind='bar', title ='Model Coefficients')
print(coeff_df)
pred = lin_reg.predict(X_test)
# # plt.scatter(y_test, pred)
test_pred = lin_reg.predict(X_test)
train_pred = lin_reg.predict(X_train)
print('Test set evaluation:\n_____________________________________')
print_evaluate(y_test, test_pred)
print('Train set evaluation:\n_____________________________________')
print_evaluate(y_train, train_pred)
results_df = pd.DataFrame(data=[["Linear Regression", *evaluate(y_test, test_pred) , cross_val(LinearRegression())]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', "Cross Validation"])
results_df
# COMMAND ----------
pred1 = lin_reg.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
# pdf
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
# x2= [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
# y2 = np.sin(x2)
# plt.figure(figsize=(8,4))
# plt.plot(x,y,'o--',color='green',lw=1,ms=5,label='SUBBU')
# plt.plot(x,y2, '-', color='red',lw=1,ms=10,label='REDDY')
plt.xlabel("DEPTS ")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** Linear Regression **",fontsize=15)
# COMMAND ----------
# print("**** Robust Regression Model****Random Sample Consensus - RANSAC****")
# from sklearn.linear_model import RANSACRegressor
# model = RANSACRegressor(base_estimator=LinearRegression(), max_trials=100)
# model.fit(X_train, y_train)
# test_pred = model.predict(X_test)
# train_pred = model.predict(X_train)
# print('Test set evaluation:\n_____________________________________')
# print_evaluate(y_test, test_pred)
# print('====================================')
# print('Train set evaluation:\n_____________________________________')
# print_evaluate(y_train, train_pred)
# results_df_2 = pd.DataFrame(data=[["Robust Regression", *evaluate(y_test, test_pred) , cross_val(RANSACRegressor())]],
# columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', "Cross Validation"])
# results_df = results_df.append(results_df_2, ignore_index=True)
# results_df_2
# COMMAND ----------
print("**** Ridge Regression Model****L2")
from sklearn.linear_model import Ridge
l2 = Ridge(alpha=100, solver='cholesky', tol=0.0001, random_state=42)
l2.fit(X_train, y_train)
pred = l2.predict(X_test)
test_pred = l2.predict(X_test)
train_pred = l2.predict(X_train)
print('Test set evaluation:\n_____________________________________')
print_evaluate(y_test, test_pred)
print('====================================')
print('Train set evaluation:\n_____________________________________')
print_evaluate(y_train, train_pred)
results_df_2 = pd.DataFrame(data=[["Ridge Regression", *evaluate(y_test, test_pred) , cross_val(Ridge())]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', "Cross Validation"])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = l2.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** L2 - Ridge Regression **",fontsize=15)
# COMMAND ----------
print("**** Lasso Regression Model**** L1")
from sklearn.linear_model import Lasso
l1 = Lasso(alpha=0.1,
precompute=True,
# warm_start=True,
positive=True,
selection='random',
random_state=42)
l1.fit(X_train, y_train)
test_pred = l1.predict(X_test)
train_pred = l1.predict(X_train)
print('Test set evaluation:\n_____________________________________')
print_evaluate(y_test, test_pred)
print('====================================')
print('Train set evaluation:\n_____________________________________')
print_evaluate(y_train, train_pred)
results_df_2 = pd.DataFrame(data=[["Lasso Regression", *evaluate(y_test, test_pred) , cross_val(Lasso())]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', "Cross Validation"])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# plt.plot(test_pred,y_test,)
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = l1.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** L1 - Lasso Regression **",fontsize=15)
# COMMAND ----------
print("**** Support Vector Machine ****")
from sklearn.svm import SVR
svm_reg = SVR(kernel='rbf', C=1000000, epsilon=0.001)
svm_reg.fit(X_train, y_train)
test_pred = svm_reg.predict(X_test)
train_pred = svm_reg.predict(X_train)
print('Test set evaluation:\n_____________________________________')
print_evaluate(y_test, test_pred)
print('Train set evaluation:\n_____________________________________')
print_evaluate(y_train, train_pred)
results_df_2 = pd.DataFrame(data=[["SVM Regressor", *evaluate(y_test, test_pred), 0]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', 'Cross Validation'])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = svm_reg.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** SVM Regression **",fontsize=15)
# COMMAND ----------
# print("**** Polynomial Regression Model****")
# from sklearn.preprocessing import PolynomialFeatures
# poly_reg = PolynomialFeatures(degree=2)
# X_train_2_d = poly_reg.fit_transform(X_train)
# X_test_2_d = poly_reg.transform(X_test)
# lin_reg = LinearRegression(normalize=True)
# lin_reg.fit(X_train_2_d,y_train)
# test_pred = lin_reg.predict(X_test_2_d)
# train_pred = lin_reg.predict(X_train_2_d)
# print('Test set evaluation:\n_____________________________________')
# print_evaluate(y_test, test_pred)
# print('====================================')
# print('Train set evaluation:\n_____________________________________')
# print_evaluate(y_train, train_pred)
# results_df_2 = pd.DataFrame(data=[["Polynomail Regression", *evaluate(y_test, test_pred), 0]],
# columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', 'Cross Validation'])
# results_df = results_df.append(results_df_2, ignore_index=True)
# results_df_2
# COMMAND ----------
print("**** Stochastic Gradient Descent Regression Model****")
from sklearn.linear_model import SGDRegressor
sgd_reg = SGDRegressor(n_iter_no_change=250, penalty=None, eta0=0.0001, max_iter=100000)
sgd_reg.fit(X_train, y_train)
test_pred = sgd_reg.predict(X_test)
train_pred = sgd_reg.predict(X_train)
print('Test set evaluation:\n_____________________________________')
print_evaluate(y_test, test_pred)
print('====================================')
print('Train set evaluation:\n_____________________________________')
print_evaluate(y_train, train_pred)
results_df_2 = pd.DataFrame(data=[["Stochastic Gradient Descent", *evaluate(y_test, test_pred), 0]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', 'Cross Validation'])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = sgd_reg.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** SGD Regression **",fontsize=15)
# COMMAND ----------
from sklearn.tree import DecisionTreeRegressor
dt_reg = DecisionTreeRegressor()
dt_reg.fit(X_train, y_train)
test_pred = dt_reg.predict(X_test)
results_df_2 = pd.DataFrame(data=[["DT Regressor", *evaluate(y_test, test_pred), 0]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', 'Cross Validation'])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = dt_reg.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** DT Regression **",fontsize=15)
# COMMAND ----------
print("**** Random Forest Regressor ****")
from sklearn.ensemble import RandomForestRegressor
rf_reg = RandomForestRegressor(n_estimators=1000)
rf_reg.fit(X_train, y_train)
test_pred = rf_reg.predict(X_test)
train_pred = rf_reg.predict(X_train)
# print('Test set evaluation:\n_____________________________________')
# print_evaluate(y_test, test_pred)
# print('Train set evaluation:\n_____________________________________')
# print_evaluate(y_train, train_pred)
results_df_2 = pd.DataFrame(data=[["RF Regressor", *evaluate(y_test, test_pred), 0]],
columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', 'Cross Validation'])
results_df = results_df.append(results_df_2, ignore_index=True)
results_df_2
# COMMAND ----------
pdf.drop("PRED_AMT", axis=1,inplace=True)
#pdf
pred1 = rf_reg.predict(X)
print(pdf.shape,X.shape,pred1.shape,pred.shape)
pdf['PRED_AMT'] = pred1
xx = pdf['DEPT_NBR'].unique()
yy = pdf.groupby("DEPT_NBR")["AMT"].sum()
# yy = pdf.groupby("DEPT_NBR")["CAN_AMT"].sum()
yy1 = pdf.groupby("DEPT_NBR")["PRED_AMT"].sum()
plt.figure(figsize=(16,8))
plt.plot(xx,yy,'o--',color='green',lw=2,ms=10,label='ACTLS')
plt.plot(xx,yy1,'o--',color='red',lw=1,ms=7,label='PRED')
plt.xlabel("DEPTS")
plt.ylabel("AMT")
plt.legend(loc='upper right',fontsize=12)
plt.title("** RF Regression **",fontsize=15)
# COMMAND ----------
results_df
# COMMAND ----------
results_df.plot.bar(x='Model', rot=45, figsize=(16, 6))
results_df=results_df.drop(index=4)
results_df.plot.bar(x='Model', rot=45, figsize=(16, 6))
# COMMAND ----------
# print(X_train.shape,X_test.shape,y_train.shape,y_test.shape,test_pred.shape,pred.shape,train_pred.shape)
# results_df
# COMMAND ----------
# COMMAND ----------
results_df['MAE'].plot.bar(x='Model', rot=0,figsize=(16, 6))
results_df['MSE'].plot.bar(x='Model', rot=0,figsize=(16, 6))
results_df['RMSE'].plot.bar(x='Model', rot=0,figsize=(16, 6))
results_df['R2 Square'].plot.bar(x='Model', rot=0,figsize=(16, 6))
# results_df.plot.box()
# COMMAND ----------
print("**** Models Comparison ****")
# results_df.set_index('Model', inplace=True)
results_df['R2 Square'].plot(kind='barh', figsize=(16, 6))
#ax = sns.plot(x="R2 Square", hue="Model", data=results_df, size=50, aspect = 8)
ax = sns.plot(x="R2 Square", hue="Model", data=results_df, size=20, aspect = 10,xlabel='R2 SQR',ylabel='Model', title='R2')
ax.set_xticklabels(rotation=30)
plt.show()