added UUID for _id and fixed the start point issue

.github/workflows/run_test_cases.yml

@@ -0,0 +1,32 @@
+name: run_test_cases
+
+on: [pull_request, push]
+
+jobs:
+  test_python:
+    runs-on: ubuntu-20.04 
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10.2' 
+
+      - run: pip install virtualenv
+
+      - run: |
+          python -m virtualenv newenv
+          source newenv/bin/activate
+
+          pip install django django-allauth pymongo setuptools --upgrade
+          pip install -r requirements.txt
+
+          python manage.py test
+          pip install coverage
+          coverage run manage.py test
+          coverage xml
+
+      - name: Upload coverage to Codecov
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} 
+        run: bash <(curl -s https://codecov.io/bash) -f coverage.xml

.gitignore

@@ -1,6 +1,7 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
 config.ini
+config.txt
 PackTravel/__pycache__/
 *.py[cod]
 *$py.class
@@ -139,4 +140,5 @@ dmypy.json
 .pytype/
 
 # Cython debug symbols
-cython_debug/
+cython_debug/
+templates/publish/route_old.html

PackTravel/settings.py

@@ -81,6 +81,10 @@
     'default': {
         'ENGINE': 'django.db.backends.sqlite3',
         'NAME': BASE_DIR / 'db.sqlite3',
+        'TEST': {
+            'NAME': 'my_test_database',  
+            'MIRROR': None,
+        },
     }
 }
 

PackTravel/urls.py

@@ -37,5 +37,9 @@
     path('accounts/', include('allauth.urls')),
     path('logout/', LogoutView.as_view()),
     path('myrides/', userView.my_rides, name = 'search'),
-    path('delete_ride/<ride_id>', userView.delete_ride, name = 'delete_ride')
+    path('delete_ride/<ride_id>', userView.delete_ride, name = 'delete_ride'),
+    path('join_ride/<ride_id>',searchViews.join_ride,name='join_ride'),
+    path('approve_rides/<ride_id>',userView.approve_rides,name='approve_rides'),
+    path('approve_user/<ride_id>/<user_id>',userView.approve_user,name='approve_user'),
+    path('ride_status/',userView.requested_rides,name='rides_status')
 ]

README.md

@@ -5,19 +5,21 @@
 
 
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7178601.svg)](https://doi.org/10.5281/zenodo.7178601)
-[![codecov](https://codecov.io/gh/amisha-w/PackTravel/branch/main/graph/badge.svg?token=HRFN97UEB7)](https://codecov.io/gh/Prachit99/PackTravel)
-![Python Style Checker](https://github.com/Prachit99/PackTravel/actions/workflows/python_style_checker.yml/badge.svg)
-![Lint Python](https://github.com/Prachit99/PackTravel/actions/workflows/pylint.yml/badge.svg)
+[![DOI](https://zenodo.org/badge/879310431.svg)](https://doi.org/10.5281/zenodo.14020210)
+![Tests](https://github.com/GradHackersGuild/PackTravel-Old/actions/workflows/run_test_cases.yml/badge.svg)
+[![Codecov](https://codecov.io/gh/GradHackersGuild/PackTravel-Old/branch/main_new/graph/badge.svg)](https://codecov.io/gh/GradHackersGuild/PackTravel-Old)
+![Python Style Checker](https://github.com/GradHackersGuild/PackTravel-Old/actions/workflows/python_style_checker.yml/badge.svg)
+![Lint Python](https://github.com/GradHackersGuild/PackTravel-Old/actions/workflows/pylint.yml/badge.svg)
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](https://opensource.org/licenses/MIT) 
-[![contributors](https://img.shields.io/github/contributors/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel/graphs/contributors)
-[![Total Lines](https://img.shields.io/tokei/lines/github/Prachit99/PackTravel?style=for-the-badge)](https://img.shields.io/tokei/lines/github/Prachit99/PackTravel)
-[![Issues](https://img.shields.io/github/issues/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel/issues)
-[![Closed Issues](https://img.shields.io/github/issues-closed-raw/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel/issues?q=is%3Aissue+is%3Aclosed)
-[![Pull Requests](https://img.shields.io/github/issues-pr/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel/pulls)
-[![Commit Acitivity](https://img.shields.io/github/commit-activity/w/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel/graphs/commit-activity)
-[![Repo Size](https://img.shields.io/github/repo-size/Prachit99/PackTravel?style=for-the-badge)](https://github.com/Prachit99/PackTravel)
+[![contributors](https://img.shields.io/github/contributors/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old/graphs/contributors)
+[![Total Lines](https://img.shields.io/tokei/lines/github/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://img.shields.io/tokei/lines/github/GradHackersGuild/PackTravel-Old)
+[![Issues](https://img.shields.io/github/issues/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old/issues)
+[![Closed Issues](https://img.shields.io/github/issues-closed-raw/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old/issues?q=is%3Aissue+is%3Aclosed)
+[![Pull Requests](https://img.shields.io/github/issues-pr/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old/pulls)
+[![Commit Activity](https://img.shields.io/github/commit-activity/w/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old/graphs/commit-activity)
+[![Repo Size](https://img.shields.io/github/repo-size/GradHackersGuild/PackTravel-Old?style=for-the-badge)](https://github.com/GradHackersGuild/PackTravel-Old)
+
 
 
 </div>

cab_model/create_store_model.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LassoCV
+import warnings
+import pickle
+from pathlib import Path
+import os
+
+BASE_DIR = Path(__file__).resolve().parent.parent
+warnings.filterwarnings('ignore')
+
+filepath = os.path.join(BASE_DIR, 'data')
+df_cabs = pd.read_csv(filepath+r"/cab_rides.csv")
+df_cabs['time_stamp'] = df_cabs['time_stamp'].astype(str).str[:-3].astype(np.int64)
+df_cabs.dropna(inplace=True)
+
+cab_company = pd.get_dummies(df_cabs.cab_type)
+cab_company.columns = ['LyftCabs', 'UberCabs']
+df_cabs = pd.concat([df_cabs, cab_company], axis=1)
+
+cab_type = pd.get_dummies(df_cabs.name)
+df_cabs = pd.concat([df_cabs, cab_type], axis=1)
+
+df_cabs['time_stamp'] = pd.to_datetime(df_cabs['time_stamp'], unit='s')
+df_cabs['timePeriodCategory'] = df_cabs.time_stamp.apply(lambda date: "EarlyMorning" if 3 <= date.hour <= 6 else "MorningNoon" if 6 < date.hour <= 17 else "Night" if 17 < date.hour <= 22 else "LateNight")
+time_period = pd.get_dummies(df_cabs.timePeriodCategory)
+df_cabs = pd.concat([df_cabs, time_period], axis=1)
+
+df_cabs['weekend'] = df_cabs['time_stamp'].dt.day_name().isin(['Saturday', 'Sunday']).astype(int)
+df_cabs['weekday'] = df_cabs.weekend.astype(bool).apply(lambda x: 0 if x == 1 else 1)
+
+df_cabs.drop(['destination', 'surge_multiplier', 'source', 'id', 'time_stamp', 'cab_type', 'product_id', 'timePeriodCategory', 'name'], axis=1, inplace=True)
+
+X = df_cabs.loc[:, df_cabs.columns != 'price']
+y = pd.DataFrame(df_cabs['price'])
+
+def lassoRegCV(x_train, y_train):
+    """
+    Lasso regression model to predict cab prices.
+    Other models such as Linear Reg., Lasso Reg., KNN Reg. and Random forest Reg. explored and tested as well. 
+    Performed different hyperparameter tuning for the models as well and data transformation such as normalizing data. 
+    Selected Lasso regression as final model based on testing error rates.
+    """
+    model_lassocv_ret = LassoCV(alphas=None, cv=10, max_iter=100000)
+    model_lassocv_ret.fit(x_train, y_train)
+    return model_lassocv_ret
+
+save_model_path = str(BASE_DIR) + "/cab_model/model.pkl"
+model_lassocv = lassoRegCV(X.to_numpy(), y.to_numpy())
+with open(save_model_path, "wb") as f:
+    pickle.dump(model_lassocv, f)

cab_model/predict.py

@@ -0,0 +1,98 @@
+"""Module to contain functionality to predict cab price"""
+from datetime import date
+import pickle
+import pandas as pd
+from pathlib import Path
+
+
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+class predict_price():
+    """Class to contain functionality to predict cab price"""
+    def __init__(self, distance, date_time_str):
+        self.distance = distance
+        self.date_time_str = date_time_str.split(" ")
+        self.date = self.date_time_str[0]
+        self.time = self.date_time_str[1]
+        self.columns = ['distance', 'LyftCabs', 'UberCabs', 'Black', 'Black SUV', 'Lux', 'Lux Black', 'Lux Black XL', 'Lyft', 'Lyft XL', 'Shared', 'UberPool', 'UberX', 'UberXL', 'WAV', 'EarlyMorning', 'LateNight', 'MorningNoon', 'Night', 'weekend', 'weekday']
+
+    def populateTimePeriod(self, to_pred_data):
+        """This method sanitizes the format of time"""
+        hour = [int(n) for n in self.time.split(":")][0]
+        if 3 <= hour and hour <= 6 :
+            to_pred_data['EarlyMorning'] = 1
+        elif 6 < hour and hour <= 17 :
+            to_pred_data['MorningNoon'] = 1
+        elif 17 < hour and hour <= 22 :
+            to_pred_data['Night'] = 1
+        else :
+            to_pred_data['LateNight'] = 1
+
+        date_list = self.date.split("-")
+        for i in range(len(date_list)):
+            if date_list[i] != "":
+                date_list[i] = int(date_list[i])
+            else:
+                date_list[i] = 0
+        d = date(day=date_list[0], month=date_list[1], year=date_list[2]).strftime('%A')
+
+        if d in ["Saturday", "Sunday"] :
+            to_pred_data["weekend"] = 1
+        else :
+            to_pred_data["weekday"] = 1
+        to_pred_data["weekend"] = 1
+
+        return to_pred_data
+
+    def dataframeFromDict(self, to_pred):
+        """This method converts a dictionary to data frame"""
+        new_data = pd.DataFrame(to_pred.items()).transpose()
+        cols = new_data.iloc[0]
+        new_data = new_data[1:]
+        new_data.columns = cols
+        return new_data
+
+
+    def predictCabs(self, to_pred):
+        """This method predicts cab price with input data"""
+        path_to_model = str(BASE_DIR) + r"/cab_model/model.pkl"
+        with open(path_to_model, 'rb') as f:
+            lasso_trained_model = pickle.load(f)
+
+        res1 = ""
+        res2 = ""
+        cabs = {
+            "UberCabs" : ['UberPool', 'Black SUV'],
+            "LyftCabs" : ['Shared', 'Lux Black XL']
+        }
+        price_range = []
+        for each_cab_comp in cabs.keys():
+            to_pred[each_cab_comp] = 1
+
+            for eachCabType in cabs[each_cab_comp]:
+                to_pred[eachCabType] = 1
+                to_pred_df = self.dataframeFromDict(to_pred)
+                price = lasso_trained_model.predict(to_pred_df.to_numpy())
+                price_range.append(price)
+                to_pred[eachCabType] = 0
+            #res += f"For {each_cab_comp}, price ranges from ${round(price_range[0][0], 2)} to ${round(price_range[1][0], 2)}\n"
+            to_pred[each_cab_comp] = 0
+        res1 = "For Uber, price range is from: $"+str(round(price_range[0][0], 2))+" to: $"+str(round(price_range[1][0], 2))
+        res2 = "For Lyft, price range is from: $"+str(round(price_range[2][0], 2))+" to: $"+str(round(price_range[3][0], 2))
+        return res1, res2
+
+    def createDataForPrice(self):
+        """This method creates data to predict cab price"""
+        to_pred = {}
+        for each in self.columns:
+            to_pred[each] = 0
+        to_pred["distance"] = self.distance
+        to_pred = self.populateTimePeriod(to_pred)
+        return to_pred
+
+
+    def generate_data_return_price(self):
+        """This method returns estimate price"""
+        data = self.createDataForPrice()
+        price_str = self.predictCabs(data)
+        return price_str