From 4a4ee41e0d3563d613020126ff6ce63979584bc6 Mon Sep 17 00:00:00 2001
From: abhisingh977 <abhis1997singh@gmail.com>
Date: Sun, 19 Apr 2020 19:47:15 -0500
Subject: [PATCH] final

---
 .../GBR.py => Gradientboost/Gradientboost.py} |  2 +-
 .../models/Gradientboost/Icon\r"              |  0
 src/models/regression/regression.py           |  2 +-
 src/models_helper.ipynb                       | 90 ++++++++++++++++++-
 4 files changed, 88 insertions(+), 6 deletions(-)
 rename src/models/{GBR/GBR.py => Gradientboost/Gradientboost.py} (98%)
 rename "src/models/GBR/Icon\r" => "src/models/Gradientboost/Icon\r" (100%)

diff --git a/src/models/GBR/GBR.py b/src/models/Gradientboost/Gradientboost.py
similarity index 98%
rename from src/models/GBR/GBR.py
rename to src/models/Gradientboost/Gradientboost.py
index 8ca6d29..80be8d4 100644
--- a/src/models/GBR/GBR.py
+++ b/src/models/Gradientboost/Gradientboost.py
@@ -40,7 +40,7 @@ def convert_to_categorical(df, categorical_variables, categories, need_pickup =
     
     x = df[['pickup_community_area' ,'temperature', 'relative_humidity', 'wind_direction', 'wind_speed', 'precipitation_cat', 
                 'sky_level', 'daytype', 'Day Name', 'Month', 'Hour', 'Fare Last Month', 'Trips Last Hour',
-                'Trips Last Week (Same Hour)', 'Trips 2 Weeks Ago (Same Hour)', 'Quarter', 'Year', 'trip_start_timestamp']]
+                'Trips Last Week (Same Hour)', 'Trips 2 Weeks Ago (Same Hour)', 'Year', 'trip_start_timestamp']]
 
     categorical_variables = ['pickup_community_area', 'daytype', 'sky_level', 'Day Name', 'Month','Hour', 'Year']
     categories = [[*(range(1,78))], ['U', 'W', 'A'], ['OVC', 'BKN', 'SCT', 'FEW', 'CLR', 'VV '], 
diff --git "a/src/models/GBR/Icon\r" "b/src/models/Gradientboost/Icon\r"
similarity index 100%
rename from "src/models/GBR/Icon\r"
rename to "src/models/Gradientboost/Icon\r"
diff --git a/src/models/regression/regression.py b/src/models/regression/regression.py
index 8ca6d29..80be8d4 100644
--- a/src/models/regression/regression.py
+++ b/src/models/regression/regression.py
@@ -40,7 +40,7 @@ def convert_to_categorical(df, categorical_variables, categories, need_pickup =
     
     x = df[['pickup_community_area' ,'temperature', 'relative_humidity', 'wind_direction', 'wind_speed', 'precipitation_cat', 
                 'sky_level', 'daytype', 'Day Name', 'Month', 'Hour', 'Fare Last Month', 'Trips Last Hour',
-                'Trips Last Week (Same Hour)', 'Trips 2 Weeks Ago (Same Hour)', 'Quarter', 'Year', 'trip_start_timestamp']]
+                'Trips Last Week (Same Hour)', 'Trips 2 Weeks Ago (Same Hour)', 'Year', 'trip_start_timestamp']]
 
     categorical_variables = ['pickup_community_area', 'daytype', 'sky_level', 'Day Name', 'Month','Hour', 'Year']
     categories = [[*(range(1,78))], ['U', 'W', 'A'], ['OVC', 'BKN', 'SCT', 'FEW', 'CLR', 'VV '], 
diff --git a/src/models_helper.ipynb b/src/models_helper.ipynb
index 869d262..151ec9d 100755
--- a/src/models_helper.ipynb
+++ b/src/models_helper.ipynb
@@ -207,9 +207,7 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "df = df.drop(columns=['Unnamed: 0'],axis=1)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -301,7 +299,91 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Gradientboost\n",
+    "model_Gradientboost = joblib.load(path_datasets+\"/model/GBR.pickle\")\n",
+    "\n",
+    "path_to_model = pathToSrc + f'{sep}models{sep}/Gradientboost/Gradientboost.py'\n",
+    "spec = importlib.util.spec_from_file_location(\"Gradientboost\", path_to_model)\n",
+    "Gradientboost = importlib.util.module_from_spec(spec)\n",
+    "spec.loader.exec_module(Gradientboost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date = \"2019-11-16 16:00:00\"\n",
+    "#getDate from request.form.values:\n",
+    "#row = df[df['trip_start_timestamp'] == date].iloc[0,]\n",
+    "\n",
+    "# Transform to array X\n",
+    "X = Gradientboost.TransformDataToX(df, date)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict\n",
+    "Y = model_Gradientboost.predict(X)\n",
+    "#prediction = np.random.randint(1,200,77)\n",
+    "# Create map\n",
+    "\n",
+    "prediction = Gradientboost.TransformYToResult(Y)\n",
+    "\n",
+    "# Create map\n",
+    "mp.mapGenerator(prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Liner_Regression\n",
+    "model_regression = joblib.load(path_datasets+\"/model/reg.pickle\")\n",
+    "\n",
+    "path_to_model = pathToSrc + f'{sep}models{sep}/regression/regression.py'\n",
+    "spec = importlib.util.spec_from_file_location(\"regression\", path_to_model)\n",
+    "regression = importlib.util.module_from_spec(spec)\n",
+    "spec.loader.exec_module(regression)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date = \"2019-11-16 16:00:00\"\n",
+    "\n",
+    "# Transform to array X\n",
+    "X = regression.TransformDataToX(df, date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict\n",
+    "Y = model_regression.predict(X)\n",
+    "#prediction = np.random.randint(1,200,77)\n",
+    "# Create map\n",
+    "\n",
+    "prediction = regression.TransformYToResult(Y)\n",
+    "\n",
+    "# Create map\n",
+    "mp.mapGenerator(prediction)"
+   ]
   }
  ],
  "metadata": {