From 4101a504030fd88cdcaa1a6e33ed06f95c21d07d Mon Sep 17 00:00:00 2001
From: amilashanaka <dsa.amilashanaka@gmail.com>
Date: Sat, 17 Dec 2022 00:47:12 +0000
Subject: [PATCH] add new

---
 API/run.py                                    |  116 +-
 ...ggle - Online Sales Training File 4.17.csv |  749 +++
 Data/data_process.py                          |    4 +-
 Data/monthly_milk_production-dataset.csv      |  169 +
 Data/sample_data.csv                          | 4020 +++++++++++++++++
 Data/sample_data.json                         |  234 +
 Report/Report_21073317.docx                   |  Bin 0 -> 57572 bytes
 model.docx                                    |  Bin 0 -> 12073 bytes
 model.png                                     |  Bin 0 -> 11249 bytes
 model.py                                      |   22 +-
 model_plot.png                                |  Bin 0 -> 11249 bytes
 model_plot_2.png                              |  Bin 0 -> 11249 bytes
 model_plot_3.png                              |  Bin 0 -> 12689 bytes
 season.py                                     |   98 +
 supervice_lernning.ipynb                      |    2 +-
 test.py                                       |   14 +
 two_node.py                                   |    8 +
 v1.ipynb                                      |  256 +-
 18 files changed, 5665 insertions(+), 27 deletions(-)
 create mode 100644 Data/Kaggle - Online Sales Training File 4.17.csv
 create mode 100644 Data/monthly_milk_production-dataset.csv
 create mode 100644 Data/sample_data.csv
 create mode 100644 Data/sample_data.json
 create mode 100644 Report/Report_21073317.docx
 create mode 100644 model.docx
 create mode 100644 model.png
 create mode 100644 model_plot.png
 create mode 100644 model_plot_2.png
 create mode 100644 model_plot_3.png
 create mode 100644 season.py
 create mode 100644 test.py
 create mode 100644 two_node.py

diff --git a/API/run.py b/API/run.py
index b3f6840..387e9fe 100644
--- a/API/run.py
+++ b/API/run.py
@@ -41,11 +41,66 @@ import matplotlib.pyplot as plt
 plt.rcParams.update({'figure.figsize':(9,7), 'figure.dpi':120})
 import pmdarima as pm
 
+#globally declare dataset
+data_set =pd.DataFrame()
+
+# Tranning dataset with
+
+tranning_data_set=pd.DataFrame()
+
+# Validation Data Set 
+
+validate_data_set=pd.DataFrame()
+
+# result data set 
+
+result_data_set=pd.DataFrame()
+
+
+def model_define():
+  # use predefined tranning data set
+  global tranning_data_set
+
+  # initialize the scaling method
+  scaler = MinMaxScaler()
+
+  #filter items an array 
+
+  items = tranning_data_set.filter(['Items'])
+  items=items.values
+
+  items_length = math.ceil(len(items)*0.8)
+  # Normalize the data set
+  scaled_data=scaler.fit_transform(items)
+
+  # Create the training dataset
+  train_data = scaled_data[0 : items_length, :]
+
+  X_train = []
+  y_train = []
+  for i in range(0, len(train_data)):
+    X_train.append(train_data[i - items_length : i, 0])
+    y_train.append(train_data[i, 0])
+
+  #  make X_train and y_train np array
+  X_train, y_train = np.array(X_train), np.array(y_train)
+
+  # reshape the data
+  X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1]))
+  print(X_train)
+
+   
+    
+  
+  return "model_define"
+ 
+
 
 @app.route('/start',methods=['POST'])
 
 def start():
 
+  global data_set
   # read incomming json data 
   data=request.get_json()
   # convert json data into pandas data structure
@@ -55,41 +110,59 @@ def start():
   df.rename(columns = {'Product_Code': 'ProductCode',
                        'Product_Category': 'ProductCategory', 
                        'Order_Demand': 'OrderDemand'}, inplace = True)
-  
-  #check is null 
-  df.isnull().sum()
 
-  # remove nan values
+  # Remove Na values from the database
   df.dropna(inplace=True)
 
-  print(df.isnull().sum())
 
 
+  # short and validate dataset
+
+  df.sort_values('Date', ignore_index=True, inplace=True)
+  df['OrderDemand'] = df['OrderDemand'].str.replace('(',"")
+  df['OrderDemand'] = df['OrderDemand'].str.replace(')',"")
+  df['OrderDemand'] = df['OrderDemand'].astype('int64')
+ 
 
+  # convert the 'Date' column to datetime format and remove not in correct date type 
+  df['Date']= pd.to_datetime(df['Date'],errors='coerce')
 
-  return data_arr
+  print(df.isna().sum())
 
+  # create Year, Month, Day columns
+  df['Year'] = df["Date"].dt.year
+  df['Month'] = df["Date"].dt.month
+  df['Day'] = df["Date"].dt.day
 
-def read_json(income_data):
 
-    X=[]
-    Y=[]
+  data_set=df
+  return_data = df.to_json()
 
-    data_arr=json.dumps(income_data)
-    din=json.loads(data_arr)
-    for i in din: 
-        X.append(i)
-        Y.append(din[i])
-        
-    data_time = np.asarray(X)
-    data_count = np.asarray(Y)
 
-    return "json"
+  return  return_data  
+
 
 @app.route('/tranning',methods=['POST'])
 
 def tranning():
-  return "tranning"
+
+  global tranning_data_set
+  
+
+
+  # read incomming json data 
+  data=request.get_json()
+
+  # convert json data into pandas data structure
+  data_arr=json.dumps(data)
+  tranning_data_set =pd.read_json(data_arr)
+  # tranning_data_set=tranning_data_set.T
+  
+  print("Tranning Start")
+
+  # model_define()
+
+  return tranning_data_set.to_json()
 
 
 @app.route('/prediction',methods=['POST'])
@@ -102,5 +175,10 @@ def prediction():
 def optimize():
   return "optimize"
 
+@app.route('/validate')
+
+def validate():
+  return "validate"
+
 if __name__ == "__main__":
     app.run()
\ No newline at end of file
diff --git a/Data/Kaggle - Online Sales Training File 4.17.csv b/Data/Kaggle - Online Sales Training File 4.17.csv
new file mode 100644
index 0000000..8b22406
--- /dev/null
+++ b/Data/Kaggle - Online Sales Training File 4.17.csv	
@@ -0,0 +1,749 @@
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diff --git a/Data/data_process.py b/Data/data_process.py
index 6a51c71..e1bfd4f 100644
--- a/Data/data_process.py
+++ b/Data/data_process.py
@@ -42,7 +42,7 @@ def make_json(csvFilePath, jsonFilePath):
         for rows in csvReader:
             # Assuming a column named 'ID' to
             # be the primary key
-            key = rows['Product_Code']
+            key = rows['Items']
             data[key] = rows
             
     
@@ -55,7 +55,7 @@ def make_json(csvFilePath, jsonFilePath):
 
 #call function convert csv file to json file format
 
-make_json('product__demnd.csv','input_data.json')
+make_json('sample_data.csv','sample_data.json')
 
 # file_data= []
 
diff --git a/Data/monthly_milk_production-dataset.csv b/Data/monthly_milk_production-dataset.csv
new file mode 100644
index 0000000..502914f
--- /dev/null
+++ b/Data/monthly_milk_production-dataset.csv
@@ -0,0 +1,169 @@
+Date,Production
+1962-01,589
+1962-02,561
+1962-03,640
+1962-04,656
+1962-05,727
+1962-06,697
+1962-07,640
+1962-08,599
+1962-09,568
+1962-10,577
+1962-11,553
+1962-12,582
+1963-01,600
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diff --git a/Data/sample_data.csv b/Data/sample_data.csv
new file mode 100644
index 0000000..105b805
--- /dev/null
+++ b/Data/sample_data.csv
@@ -0,0 +1,4020 @@
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diff --git a/Data/sample_data.json b/Data/sample_data.json
new file mode 100644
index 0000000..7a1484e
--- /dev/null
+++ b/Data/sample_data.json
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\ No newline at end of file
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diff --git a/model.py b/model.py
index c2f9197..80b5eb1 100644
--- a/model.py
+++ b/model.py
@@ -18,6 +18,10 @@ warnings.filterwarnings("ignore")
 
 from datetime import datetime as dt
 
+from keras.utils.vis_utils import plot_model
+
+from statsmodels.tsa.seasonal import seasonal_decompose
+
 
 data = pd.read_csv("data/product__demnd.csv")
 print(data)
@@ -57,6 +61,8 @@ print(data.info())
 
 print(data["OrderDemand"].describe())
 
+
+
 # information about categorical variables
 print(data[["ProductCode", "Warehouse", "ProductCategory"]].describe())
 
@@ -142,6 +148,9 @@ print(df)
 print(df.describe())
 
 
+
+
+
 #Product Category Based Analysis
 
 
@@ -175,13 +184,13 @@ orderD_array = orderD.values
 train_close_len = math.ceil(len(orderD_array) * 0.8)
 print(train_close_len)
 
-
+print("normalize data set /////////////////////////////////")
 # Normalize the data
 scaler = MinMaxScaler()
 scaled_data = scaler.fit_transform(orderD_array)
 print(scaled_data)
 
-
+print("tranning dataset =====================================================")
 
 # Create the training dataset
 train_data = scaled_data[0 : train_close_len, :]
@@ -212,6 +221,11 @@ for i in range(60, len(test_data)):
     X_test.append(test_data[i - 60 : i, 0])
 
 
+print("Model input data ffffffffffffffffffffff")
+
+
+
+
 # convert the test data to a np array and reshape the test data
 X_test = np.array(X_test)
 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
@@ -233,7 +247,7 @@ model.add(Dense(units=1))
 # compile the LSTM model
 model.compile(optimizer="Adam", loss="mean_squared_error", metrics=['mae'])
 
-
+plot_model(model, to_file='model_plot_3.png', show_shapes=True, show_layer_names=True)
 
 # train the LSTM model
 model.fit(X_train, y_train,
@@ -243,6 +257,8 @@ model.fit(X_train, y_train,
 
 
 
+print(model.summary())
+
 # predict with LSTM model
 predictions = model.predict(X_test)
 predictions = scaler.inverse_transform(predictions)
diff --git a/model_plot.png b/model_plot.png
new file mode 100644
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diff --git a/season.py b/season.py
new file mode 100644
index 0000000..7e227f5
--- /dev/null
+++ b/season.py
@@ -0,0 +1,98 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from statsmodels.tsa.seasonal import seasonal_decompose
+from sklearn.preprocessing import MinMaxScaler
+
+from keras.preprocessing.sequence import TimeseriesGenerator
+
+from keras.layers import LSTM
+from keras.models import Sequential
+from keras.layers import Dense
+
+import numpy as np
+
+
+#  https://www.section.io/engineering-education/univariate-time-series-using-recurrent-neural-networks/
+
+df = pd.read_csv('data/sample_data.csv',index_col='Date',parse_dates=True)
+
+print(df)
+
+df.index.freq='D'
+
+print(df.index.freq)
+
+df.plot(figsize=(12,6))
+
+plt.show()
+
+decomposable_components = seasonal_decompose(df['Items'])
+decomposable_components.plot()
+
+plt.show()
+
+
+train_dates = df.iloc[:156]
+test_dates = df.iloc[156:]
+
+print(train_dates)
+print(test_dates)
+
+scaler = MinMaxScaler()
+
+scaler.fit(train_dates)
+scaler.fit(test_dates)
+
+scaled_train_dates = scaler.transform(train_dates)
+scaled_test_dates = scaler.transform(test_dates)
+
+print(scaled_train_dates)
+
+scaled_train_dates[:10]
+
+n_input = 12
+n_features = 1
+
+generated_batches = TimeseriesGenerator(scaled_train_dates, scaled_test_dates, length=n_input, batch_size=1)
+
+
+
+# lstm_model = Sequential()
+
+# lstm_model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
+
+# lstm_model.add(Dense(1))
+
+# lstm_model.compile(optimizer='adam', loss='mse')
+
+# print(lstm_model.summary())
+
+# lstm_model.fit(generated_batches,epochs=100)
+
+# prediction_result = []
+
+# test_batches = scaled_train_dates[-n_input:]
+# reshaping_batches = test_batches.reshape((1, n_input, n_features))
+
+
+# for i in range(len(test_dates)):
+
+#     predicted_output = lstm_model.predict(reshaping_batches )[0]
+
+#     prediction_result.append(predicted_output) 
+
+#     reshaping_batches = np.append(reshaping_batches [:,1:,:],[[predicted_output]],axis=1)
+
+
+
+
+
+
+
+# print(prediction_result)
+
+# actual_values = scaler.inverse_transform(prediction_result)
+# test_dates['Predictions'] = actual_values
+# test_dates.plot(figsize=(14,5))
+
+# plt.show()
\ No newline at end of file
diff --git a/supervice_lernning.ipynb b/supervice_lernning.ipynb
index 202c4a2..9da6c35 100644
--- a/supervice_lernning.ipynb
+++ b/supervice_lernning.ipynb
@@ -592,7 +592,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..40b585e
--- /dev/null
+++ b/test.py
@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+
+
+SampleOne, Times = get_idletime_set(1000)
+FMT = '%Y-%m-%d %H:%M:%S'
+Dates=[]
+for i in Times:
+    Dates.append(datetime.strptime(i, FMT))
+
+
+plt.plot(Dates, SampleOne)
+plt.ylabel('Idle Duration')
+plt.xlabel('Time')
+plt.show()
\ No newline at end of file
diff --git a/two_node.py b/two_node.py
new file mode 100644
index 0000000..8d687e8
--- /dev/null
+++ b/two_node.py
@@ -0,0 +1,8 @@
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils.vis_utils import plot_model
+model = Sequential()
+model.add(Dense(2, input_dim=1, activation='relu'))
+model.add(Dense(1, activation='sigmoid'))
+plot_model(model, to_file='model_plot_2.png', show_shapes=True, show_layer_names=True)
+
diff --git a/v1.ipynb b/v1.ipynb
index d5c2000..0092cb6 100644
--- a/v1.ipynb
+++ b/v1.ipynb
@@ -1,10 +1,254 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read call center data from MySql and dand make daily call count , total talk time , drop call count "
+    "Get data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Items</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011-01-01</th>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-02</th>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-03</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-04</th>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011-01-05</th>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-12-28</th>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-12-29</th>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-12-30</th>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2021-12-31</th>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-01-01</th>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4019 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Items\n",
+       "Date             \n",
+       "2011-01-01     30\n",
+       "2011-01-02     34\n",
+       "2011-01-03     41\n",
+       "2011-01-04     45\n",
+       "2011-01-05     49\n",
+       "...           ...\n",
+       "2021-12-28     35\n",
+       "2021-12-29     45\n",
+       "2021-12-30     47\n",
+       "2021-12-31     21\n",
+       "2022-01-01     38\n",
+       "\n",
+       "[4019 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
+    "\n",
+    "from keras.layers import LSTM\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv('data/sample_data.csv',index_col='Date',parse_dates=True)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "plot data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Day>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Date'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.index.freq='D'\n",
+    "\n",
+    "print(df.index.freq)\n",
+    "\n",
+    "df.plot(figsize=(12,6))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Decomposing the time series into components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decomposable_components = seasonal_decompose(df['Items'])\n",
+    "decomposable_components.plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Splitting the time series dataset into training and testing dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dates = df.iloc[:156]\n",
+    "test_dates = df.iloc[156:]\n",
+    "\n",
+    "train_dates\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "\n",
+    "scaler.fit(train_dates)\n",
+    "scaler.fit(test_dates)\n"
    ]
   }
  ],
@@ -15,8 +259,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.10.8"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
   },
   "orig_nbformat": 4,
   "vscode": {
-- 
GitLab