Merge Final Commits from powerpuffs

prd_analysis/generator.py

@@ -0,0 +1,122 @@
+import random
+from datetime import datetime
+from dateutil.parser import parse
+import csv
+import xgboost as xgb
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from datetime import datetime
+import json
+
+color_pal = sns.color_palette()
+plt.style.use('fivethirtyeight')
+
+data_path = "./prd-sales.csv"
+
+months_list = [
+    'January', "Febuary", "March", "April", "May", "June", "July", "August", "Septemnber", "October", "November", "December"
+]
+# Get data from drive
+df = pd.read_csv(data_path, index_col=False)
+
+# Fill the null values by 0 and convert the numbers by float
+df['Sale'] = df['Sale'].fillna(0)
+df['totalSale'] = df['Sale'].apply(lambda x: 0 if x == 0 or x == '' or x == ' ' else float(
+    str(x).replace(',', '').replace(' ', '')))
+# Create date as index / key
+df = df.set_index('Date')
+df.index = pd.to_datetime(df.index)
+
+# Sort the value by Date
+df = df.sort_values(by="Date")
+re = df
+
+
+def predict_future(model, begin_date, days=7):
+    df_preds_future = pd.DataFrame(
+        {'totalSale': 0, 'Date': pd.date_range(begin_date, periods=days)})
+    df_preds_future = df_preds_future.set_index('Date')
+    df_preds_future.index = pd.to_datetime(df_preds_future.index)
+    df_preds_future = df_preds_future.sort_values(by="Date")
+    X, y = create_features(df_preds_future, 'totalSale')
+    predicted_results_future = model.predict(X)
+    X['totalSale'] = predicted_results_future
+    return X
+
+
+def create_features(df, target_variable):
+    df['date'] = df.index
+    df['hour'] = df['date'].dt.hour
+    df['dayofweek'] = df['date'].dt.dayofweek
+    df['quarter'] = df['date'].dt.quarter
+    df['month'] = df['date'].dt.month
+    df['year'] = df['date'].dt.year
+    df['dayofyear'] = df['date'].dt.dayofyear
+    df['dayofmonth'] = df['date'].dt.day
+    df['weekofyear'] = df['date'].dt.weekofyear
+    X = df[['hour', 'dayofweek', 'quarter', 'month',
+            'year', 'dayofyear', 'dayofmonth', 'weekofyear']]
+    if target_variable:
+        y = df[target_variable]
+        return X, y
+    return X
+
+
+def days_between(d1, d2):
+    d1 = datetime.strptime(d1, date_time_format)
+    d2 = datetime.strptime(d2, date_time_format)
+    return abs((d2 - d1).days)
+
+
+date_time_format = "%Y-%m-%d %H:%M:%S"
+datew_time_format = "%Y-%m-%d"
+
+csv_date = df.index.date.max().strftime(date_time_format)
+date_now = datetime.now().strftime(date_time_format)
+oldest_date = csv_date if parse(csv_date) < parse(date_now) else date_now
+
+model_path = 'prd-sales.bin'
+xgboost_model = xgb.XGBRegressor()
+xgboost_model.load_model(model_path)
+
+preds = predict_future(xgboost_model, oldest_date,
+                       days_between(csv_date, date_now))
+df_preds_future = preds.sort_values(by="Date")
+
+
+dates = []
+totalSale = []
+for index, row in df_preds_future.iterrows():
+    dates.append(index)
+    value = int(row['totalSale'])
+    res = random.randint(value-100, value+300)
+    totalSale.append(res)
+
+
+new_value = {
+    'totalSale': totalSale
+}
+df_res = pd.DataFrame(new_value, index=dates)
+df_all_rows = pd.concat([df, df_res])
+plt.show()
+
+result = []
+x = False
+for index, row in df_all_rows.iterrows():
+
+    if (re.index[-1] == index) and x == False:
+        x = True
+        continue
+    result.append({
+        "datew":  index.strftime(datew_time_format),
+        "date":  index.strftime(date_time_format),
+        "totalSale":  row['totalSale']
+    })
+
+
+# Function to convert a CSV to JSON
+# Takes the file paths as arguments
+jsonFilePath = 'Dataset.json'
+with open(jsonFilePath, 'w', encoding='utf-8') as jsonf:
+    jsonf.write(json.dumps(result, indent=4))