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import numpy as np
import matplotlib.pyplot as plt
import xgboost as xgb
import pickle
import os
import pandas as pd
# EDA
def check_dates(df, end_date):
"""
Checks that the dataframe is in correct order
"""
months_31 = {"01", "03", "05", "07", "08", "10", "12"}
months_30 = {"04", "06", "09", "11"}
months_28 = {"02"}
for idx, row in df.iterrows():
if(row["month_str"] == end_date):
continue
if(row["month_str"][5:7] in months_31):
if (row["interval"] != np.timedelta64(31, "D")):
return False
if(row["month_str"][5:7] in months_30):
if (row["interval"] != np.timedelta64(30, "D")):
return False
if(row["month_str"][5:7] in months_28):
if (row["interval"] != np.timedelta64(28, "D") and int(row["month_str"][:4]) % 4 != 0):
return False
# Leap Year
if (row["interval"] != np.timedelta64(29, "D") and int(row["month_str"][:4]) % 4 == 0):
return False
return True
# EDA
def plot_cohort(df, cohort_first_month, product):
"""
Plots the specified cohort given product and the date of the cohort
"""
df_ = get_sequence(df, cohort_first_month, product)
x = np.array([x for x in range(df_.shape[0])])
fig = plt.figure()
ax = fig.gca()
plt.plot(x, df_["percentage"])
plt.grid()
ax.set_xlim([0, df_.shape[0]])
ax.set_ylim([0, 1])
ax.set_xlabel("Months")
ax.set_ylabel("Percentage")
ttle = f"{cohort_first_month} | {product}"
ax.set_title(ttle)
plt.show()
##########################################################################################
# train
def plot_feature_importance(model, feature_names):
"""
Plots the importance of the features of a XGB model
"""
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
names = [feature_names[i] for i in indices]
plt.figure(figsize=(10, 6))
plt.title("Feature Importance")
plt.bar(range(len(importances)), importances[indices])
plt.xticks(range(len(importances)), names, rotation=90)
plt.show()
##########################################################################################
# evaluate
def get_sequence(df, cohort_first_month, product):
"""
Gets the dataframe of a sequence given the product and the date of the cohort
"""
df_ = df[df["cohort_first_month"] == cohort_first_month]
df_ = df_[df_["cohort_first_product"] == product]
return df_
# evaluate
def plot_true_and_predicted(y_true, y_pred, cohort, product):
"""
Plots the true and predicted time-series given a cohort and a product
Every step is of the predicted is given the true t-1 datapoint. Its does not
create an entire sequence from predictions.
"""
x = np.array([x for x in range(y_true.shape[0])])
fig = plt.figure()
ax = fig.gca()
plt.plot(x, y_true, label="Y True")
plt.plot(x, y_pred, label="Y Pred")
plt.grid()
ax.set_xlim([0, y_true.shape[0]])
ax.set_ylim([0, 1])
ax.set_xlabel("Months")
ax.set_ylabel("Percentage")
ttle = f"{cohort} | {product}"
ax.set_title(ttle)
ax.legend()
plt.show()
# evaluate
def get_product_one_hot_encode(product):
"""
Gets a one hot encoded dataframe of the possible products for a row
"""
products = {"1m":0,"3m":0,"4m":0}
columns = ["product_1m", "product_3m", "product_4m"]
products[product] = 1
df = pd.DataFrame([products])
df = df.rename(columns = {"1m": columns[0],
"3m": columns[1],
"4m": columns[2]})
# print(df)
return df
# evaluate
def get_month_one_hot_encode(month):
"""
Gets a one hot encoded dataframe of the months
"""
months = [0 for x in range(12)]
columns = [f"month_{x}" for x in range(1,13)]
months[month-1] = 1
df = pd.DataFrame([months], columns=columns)
# print(df)
return df
# evaluate
def generate_new_data(df, date, cohort, product, model, columns_to_drop, n_points):
"""
This function generates data for a cohort of a product, from a specified date.
It will use the predicion model, to generate the n consequent time steps of a cohort.
The datapoints will be generated given the previously generated datapoints, in an iterative
fashion
"""
df_ = df[df["cohort_first_month"] == cohort]
df_ = df_[df_["cohort_first_product"] == product]
df_ = df_[df_["month"] == date]
current_month = int(date[5:7])
current_msa = df_["months_since_acquisition"].values[0]
df_ = df_.drop(columns=columns_to_drop)
columns = df_.columns
product_ohe = get_product_one_hot_encode(product)
datapoint = df_.copy()
counter = 0
while(counter < n_points):
prediction = model.predict(datapoint)
# print(prediction)
current_month = (current_month%12)+1
month_ohe = get_month_one_hot_encode(current_month)
current_msa += 1
new_row = pd.DataFrame([current_msa], columns=[columns[0]])
new_row[columns[1]] = prediction[0]
new_row = new_row.join(product_ohe)
new_row = new_row.join(month_ohe)
df_ = pd.concat([df_,new_row], ignore_index=True)
datapoint = new_row.copy()
counter +=1
return df_
# evaluate
def plot_example_from_case(historical, predicted, x_lim, product):
"""
With the generated data, it plots the historical true data, and in a dotted line
the data that was predicted by the model for the subsequent datapoints.
"""
x_historical = np.array([x for x in range(historical.shape[0])])
x_predicted= np.array([x + historical.shape[0]-1 for x in range(predicted.shape[0])])
y_historical = historical["percentage"]
y_predicted = predicted["percentage"]
cohort_date = historical.iloc[0]["cohort_first_month"].strftime('%Y-%m-%d')
fig = plt.figure()
ax = fig.gca()
plt.plot(x_historical, y_historical, label="historical", color="blue", linestyle="-")
plt.plot(x_predicted, y_predicted, label="predicted", color="blue", linestyle="--")
plt.grid()
ax.set_xlim([0, x_lim])
ax.set_ylim([0, 1])
ax.set_xlabel("Months")
ax.set_ylabel("Percentage")
ttle = f" Cohort {cohort_date} | Product {product}"
ax.set_title(ttle)
ax.legend()
plt.show()
return fig |