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README.md
浏览文件 @ 1d39410d
# PyCaret
# PyCaret
[![Stability](https://img.shields.io/badge/stability-stable-green.svg)](https://img.shields.io/badge/stability-stable-green.svg) [![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable) [![PyPI version](https://badge.fury.io/py/pycaret.svg)](https://badge.fury.io/py/pycaret) [![License](https://img.shields.io/pypi/l/ansicolortags.svg)](https://img.shields.io/pypi/l/ansicolortags.svg) [![Git count](http://hits.dwyl.com/pycaret/pycaret/pycaret.svg)](http://hits.dwyl.com/pycaret/pycaret/pycaret)
## What is PyCaret?
......@@ -34,23 +34,6 @@ PyCaret is an open source library that anybody can use. In our view the ideal ta
Please read requirements.txt for list of requirements. They are automatically installed when pycaret is installed using pip.
## Note:
It supports Python 64 bit only.
<<<<<<< HEAD
<<<<<<< HEAD
## Authors
- pycaret.anomaly, Moez Ali <moez.ali@queensu.ca>
- pycaret.classification, Moez Ali <moez.ali@queensu.ca>
- pycaret.clustering, Moez Ali <moez.ali@queensu.ca>
- pycaret.datasets, Moez Ali <moez.ali@queensu.ca>
- pycaret.nlp, Moez Ali <moez.ali@queensu.ca>
- pycaret.preprocess, Fahad Akbar <M.akbar@queensu.ca>
- pycaret.regression, Moez Ali <moez.ali@queensu.ca>
If you would like to contribute to PyCaret, please visit https://www.pycaret.org/contribute
=======
>>>>>>> dev-1.0.1
=======
>>>>>>> dev-1.0.1
## License
......
Use Cases/Gold Prediction/Classification/Gold Prediction New Data - Classification.ipynb 已删除 100644 → 0
浏览文件 @ 09e669a9
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Importing Libraries\n",
"import pandas as pd\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"from yahoofinancials import YahooFinancials"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"ticker_details = pd.read_excel(\"Ticker List.xlsx\")\n",
"ticker = ticker_details['Ticker'].to_list()\n",
"names = ticker_details['Description'].to_list()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Preparing Date Range\n",
"end_date= datetime.strftime(datetime.today(),'%Y-%m-%d')\n",
"start_date = \"2019-01-01\"\n",
"date_range = pd.bdate_range(start=start_date,end=end_date)\n",
"values = pd.DataFrame({ 'Date': date_range})\n",
"values['Date']= pd.to_datetime(values['Date'])\n",
"\n",
"#Extracting Data from Yahoo Finance and Adding them to Values table using date as key\n",
"for i in ticker:\n",
" raw_data = YahooFinancials(i)\n",
" raw_data = raw_data.get_historical_price_data(start_date, end_date, \"daily\")\n",
" df = pd.DataFrame(raw_data[i]['prices'])[['formatted_date','adjclose']]\n",
" df.columns = ['Date1',i]\n",
" df['Date1']= pd.to_datetime(df['Date1'])\n",
" values = values.merge(df,how='left',left_on='Date',right_on='Date1')\n",
" values = values.drop(labels='Date1',axis=1)\n",
"\n",
"#Renaming columns to represent instrument names rather than their ticker codes for ease of readability\n",
"names.insert(0,'Date')\n",
"values.columns = names\n",
"\n",
"#Front filling the NaN values in the data set\n",
"values = values.fillna(method=\"ffill\",axis=0)\n",
"values = values.fillna(method=\"bfill\",axis=0)\n",
"\n",
"# Co-ercing numeric type to all columns except Date\n",
"cols=values.columns.drop('Date')\n",
"values[cols] = values[cols].apply(pd.to_numeric,errors='coerce').round(decimals=1)\n",
"imp = ['Gold','Silver', 'Crude Oil', 'S&P500','MSCI EM ETF']\n",
"\n",
"# Calculating Short term -Historical Returns\n",
"change_days = [1,3,5,14,21]\n",
"\n",
"data = pd.DataFrame(data=values['Date'])\n",
"for i in change_days:\n",
" x= values[cols].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
" data=pd.concat(objs=(data,x),axis=1)\n",
" x=[]\n",
"\n",
"# Calculating Long term Historical Returns\n",
"change_days = [60,90,180,250]\n",
"\n",
"for i in change_days:\n",
" x= values[imp].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
" data=pd.concat(objs=(data,x),axis=1)\n",
" x=[]\n",
"\n",
"#Calculating Moving averages for Gold\n",
"moving_avg = pd.DataFrame(values['Date'],columns=['Date'])\n",
"moving_avg['Date']=pd.to_datetime(moving_avg['Date'],format='%Y-%b-%d')\n",
"moving_avg['Gold/15SMA'] = (values['Gold']/(values['Gold'].rolling(window=15).mean()))-1\n",
"moving_avg['Gold/30SMA'] = (values['Gold']/(values['Gold'].rolling(window=30).mean()))-1\n",
"moving_avg['Gold/60SMA'] = (values['Gold']/(values['Gold'].rolling(window=60).mean()))-1\n",
"moving_avg['Gold/90SMA'] = (values['Gold']/(values['Gold'].rolling(window=90).mean()))-1\n",
"moving_avg['Gold/180SMA'] = (values['Gold']/(values['Gold'].rolling(window=180).mean()))-1\n",
"moving_avg['Gold/90EMA'] = (values['Gold']/(values['Gold'].ewm(span=90,adjust=True,ignore_na=True).mean()))-1\n",
"moving_avg['Gold/180EMA'] = (values['Gold']/(values['Gold'].ewm(span=180,adjust=True,ignore_na=True).mean()))-1\n",
"moving_avg = moving_avg.dropna(axis=0)\n",
"#Merging Moving Average values to the feature space\n",
"\n",
"data['Date']=pd.to_datetime(data['Date'],format='%Y-%b-%d')\n",
"data = pd.merge(left=data,right=moving_avg,how='left',on='Date')\n",
"data = data[data['Gold-T-250'].notna()]\n",
"prediction_data = data.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from pycaret.classification import *\n",
"#Loading the stored model\n",
"classifier_22 = load_model(\"22D Classifier\");\n",
"#Making Predictions\n",
"prediction = predict_model(classifier_22,data=prediction_data)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Label</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2020-04-16</td>\n",
" <td>0</td>\n",
" <td>0.0869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2020-04-17</td>\n",
" <td>0</td>\n",
" <td>0.1266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2020-04-20</td>\n",
" <td>0</td>\n",
" <td>0.1037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2020-04-21</td>\n",
" <td>0</td>\n",
" <td>0.1489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>2020-04-22</td>\n",
" <td>0</td>\n",
" <td>0.0860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>2020-04-23</td>\n",
" <td>0</td>\n",
" <td>0.0823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>2020-04-24</td>\n",
" <td>0</td>\n",
" <td>0.1129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>2020-04-27</td>\n",
" <td>0</td>\n",
" <td>0.0976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>2020-04-28</td>\n",
" <td>0</td>\n",
" <td>0.1023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>2020-04-29</td>\n",
" <td>0</td>\n",
" <td>0.0919</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Label Score\n",
"87 2020-04-16 0 0.0869\n",
"88 2020-04-17 0 0.1266\n",
"89 2020-04-20 0 0.1037\n",
"90 2020-04-21 0 0.1489\n",
"91 2020-04-22 0 0.0860\n",
"92 2020-04-23 0 0.0823\n",
"93 2020-04-24 0 0.1129\n",
"94 2020-04-27 0 0.0976\n",
"95 2020-04-28 0 0.1023\n",
"96 2020-04-29 0 0.0919"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction[['Date','Label','Score']].tail(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
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"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Use Cases/Gold Prediction/Classification/Read me 已删除 100644 → 0
浏览文件 @ 09e669a9
In this experiment, we will try to predict if there would be a ‘sharp fall’ or ‘crash’ in Gold Prices over next 22-Day period.
We would be using a classification technique for this experiment. We will also learn how to use the trained model to make a prediction on new data every day.
The steps for this exercise would be:
1. Import and shape the data — This would be similar to what was explained in the Regression Experiment Notebook. You can also use the final Training dataset from the Data folder.
2. Define ‘sharp fall’ in Gold Prices. Sharp is not an absolute measure. We will try to define ‘sharp fall’ objectively
3. Create Label — Based on the definition of ‘sharp fall’, we would create labels on historical data
4. Train models to predict the ‘sharp fall’ and use trained model to make prediction on new data
Detailed article explaining the experiment can be found here ( https://towardsdatascience.com/predicting-crashes-in-gold-prices-using-machine-learning-5769f548496 )
Use Cases/Gold Prediction/Data/Training Data_Values.csv 已删除 100644 → 0
浏览文件 @ 09e669a9
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
Use Cases/Gold Prediction/Regression/Gold Prediction New Data - Regression.ipynb 已删除 100644 → 0
浏览文件 @ 09e669a9
{
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"#Importing Libraries\n",
"import pandas as pd\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"from yahoofinancials import YahooFinancials"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"ticker_details = pd.read_excel(\"Ticker List.xlsx\")\n",
"ticker = ticker_details['Ticker'].to_list()\n",
"names = ticker_details['Description'].to_list()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"#Preparing Date Range\n",
"end_date= datetime.strftime(datetime.today(),'%Y-%m-%d')\n",
"start_date = \"2019-01-01\"\n",
"date_range = pd.bdate_range(start=start_date,end=end_date)\n",
"values = pd.DataFrame({ 'Date': date_range})\n",
"values['Date']= pd.to_datetime(values['Date'])\n",
"\n",
"#Extracting Data from Yahoo Finance and Adding them to Values table using date as key\n",
"for i in ticker:\n",
" raw_data = YahooFinancials(i)\n",
" raw_data = raw_data.get_historical_price_data(start_date, end_date, \"daily\")\n",
" df = pd.DataFrame(raw_data[i]['prices'])[['formatted_date','adjclose']]\n",
" df.columns = ['Date1',i]\n",
" df['Date1']= pd.to_datetime(df['Date1'])\n",
" values = values.merge(df,how='left',left_on='Date',right_on='Date1')\n",
" values = values.drop(labels='Date1',axis=1)\n",
"\n",
"#Renaming columns to represent instrument names rather than their ticker codes for ease of readability\n",
"names.insert(0,'Date')\n",
"values.columns = names\n",
"\n",
"#Front filling the NaN values in the data set\n",
"values = values.fillna(method=\"ffill\",axis=0)\n",
"values = values.fillna(method=\"bfill\",axis=0)\n",
"\n",
"# Co-ercing numeric type to all columns except Date\n",
"cols=values.columns.drop('Date')\n",
"values[cols] = values[cols].apply(pd.to_numeric,errors='coerce').round(decimals=1)\n",
"imp = ['Gold','Silver', 'Crude Oil', 'S&P500','MSCI EM ETF']\n",
"\n",
"# Calculating Short term -Historical Returns\n",
"change_days = [1,3,5,14,21]\n",
"\n",
"data = pd.DataFrame(data=values['Date'])\n",
"for i in change_days:\n",
" x= values[cols].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
" data=pd.concat(objs=(data,x),axis=1)\n",
" x=[]\n",
"\n",
"# Calculating Long term Historical Returns\n",
"change_days = [60,90,180,250]\n",
"\n",
"for i in change_days:\n",
" x= values[imp].pct_change(periods=i).add_suffix(\"-T-\"+str(i))\n",
" data=pd.concat(objs=(data,x),axis=1)\n",
" x=[]\n",
"\n",
"#Calculating Moving averages for Gold\n",
"moving_avg = pd.DataFrame(values['Date'],columns=['Date'])\n",
"moving_avg['Date']=pd.to_datetime(moving_avg['Date'],format='%Y-%b-%d')\n",
"moving_avg['Gold/15SMA'] = (values['Gold']/(values['Gold'].rolling(window=15).mean()))-1\n",
"moving_avg['Gold/30SMA'] = (values['Gold']/(values['Gold'].rolling(window=30).mean()))-1\n",
"moving_avg['Gold/60SMA'] = (values['Gold']/(values['Gold'].rolling(window=60).mean()))-1\n",
"moving_avg['Gold/90SMA'] = (values['Gold']/(values['Gold'].rolling(window=90).mean()))-1\n",
"moving_avg['Gold/180SMA'] = (values['Gold']/(values['Gold'].rolling(window=180).mean()))-1\n",
"moving_avg['Gold/90EMA'] = (values['Gold']/(values['Gold'].ewm(span=90,adjust=True,ignore_na=True).mean()))-1\n",
"moving_avg['Gold/180EMA'] = (values['Gold']/(values['Gold'].ewm(span=180,adjust=True,ignore_na=True).mean()))-1\n",
"moving_avg = moving_avg.dropna(axis=0)\n",
"#Merging Moving Average values to the feature space\n",
"\n",
"data['Date']=pd.to_datetime(data['Date'],format='%Y-%b-%d')\n",
"data = pd.merge(left=data,right=moving_avg,how='left',on='Date')\n",
"data = data[data['Gold-T-250'].notna()]\n",
"prediction_data = data.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Gold</th>\n",
" <th>Return_22</th>\n",
" <th>Gold-T+22</th>\n",
" <th>Date-T+22</th>\n",
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" <tr>\n",
" <th>84</th>\n",
" <td>2020-04-13</td>\n",
" <td>1769.4</td>\n",
" <td>0.0406</td>\n",
" <td>1841.2</td>\n",
" <td>2020-05-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>2020-04-14</td>\n",
" <td>1751.3</td>\n",
" <td>0.0188</td>\n",
" <td>1784.2</td>\n",
" <td>2020-05-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2020-04-15</td>\n",
" <td>1743.3</td>\n",
" <td>-0.0068</td>\n",
" <td>1731.4</td>\n",
" <td>2020-05-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2020-04-16</td>\n",
" <td>1730.1</td>\n",
" <td>-0.0096</td>\n",
" <td>1713.5</td>\n",
" <td>2020-05-08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2020-04-17</td>\n",
" <td>1694.5</td>\n",
" <td>-0.0233</td>\n",
" <td>1655.0</td>\n",
" <td>2020-05-09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Gold Return_22 Gold-T+22 Date-T+22\n",
"84 2020-04-13 1769.4 0.0406 1841.2 2020-05-05\n",
"85 2020-04-14 1751.3 0.0188 1784.2 2020-05-06\n",
"86 2020-04-15 1743.3 -0.0068 1731.4 2020-05-07\n",
"87 2020-04-16 1730.1 -0.0096 1713.5 2020-05-08\n",
"88 2020-04-17 1694.5 -0.0233 1655.0 2020-05-09"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pycaret.regression import *\n",
"#Loading the stored model\n",
"regressor_22 = load_model(\"22Day Regressor\");\n",
"#Making Predictions\n",
"predicted_return_22 = predict_model(regressor_22,data=prediction_data)\n",
"predicted_return_22=predicted_return_22[['Date','Label']]\n",
"predicted_return_22.columns = ['Date','Return_22']\n",
"\n",
"\n",
"#Adding return Predictions to Gold Values\n",
"predicted_values = values[['Date','Gold']]\n",
"predicted_values = predicted_values.tail(len(predicted_return_22))\n",
"predicted_values = pd.merge(left=predicted_values,right=predicted_return_22,on=['Date'],how='inner')\n",
"predicted_values['Gold-T+22']=(predicted_values['Gold']*(1+predicted_values['Return_22'])).round(decimals =1)\n",
"#Adding T+22 Date\n",
"from datetime import datetime, timedelta\n",
"predicted_values['Date-T+22'] = predicted_values['Date']+timedelta(days = 22)\n",
"predicted_values.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"pygments_lexer": "ipython3",
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Use Cases/Gold Prediction/Regression/Read me 已删除 100644 → 0
浏览文件 @ 09e669a9
In this experiemnet we will try to use a multi-variate regression approach to forecast the return from Gold.
In the notebook, you will find codes that help you download financial information from yahoofinancials API for free and we will go through
how to shape and prepare the data to make it fit for prediction.
We will use a list of tickers which can be found in the Tickers.xlsx file in the Data folder, to download the historical prices.
Detailed article on the experiment can be found here (https://towardsdatascience.com/machine-learning-to-predict-gold-price-returns-4bdb0506b132 )
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import streamlit as st
from pycaret.regression import *
import matplotlib.pyplot as plt
from PIL import Image
import pandas as pd
#import os.path
#import sys
#sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
st.balloons()
add_selectbox = st.sidebar.selectbox(
'Menu',
('About', 'Upload Data', 'Base Models', 'Model Analysis', 'Predictions'))
image = Image.open('logo.png')
st.image(image)
st.warning("PyCaret 'UI' is a beta release announced with PyCaret version 1.0.1. (Not for production)")
st.markdown("## **What is PyCaret?**")
st.markdown("PyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently. In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to perform complex machine learning tasks with only few lines of code.")
st.markdown("## **What is PyCaret UI?**")
st.markdown("PyCaret UI is front-end interface designed on streamlit. This is ideal for data professionals who prefers a no-code solution.")
st.markdown("## **Important Links**")
st.markdown("Official : https://www.pycaret.org")
st.markdown("GitHub : https://www.github.com/pycaret/pycaret")
st.markdown("YouTube : https://www.youtube.com/channel/UCxA1YTYJ9BEeo50lxyI_B3g")
st.markdown("LinkedIn : https://www.linkedin.com/company/pycaret")
#st.title('PyCaret AutoML Interface')
from pycaret.datasets import get_data
data = get_data('boston')
#st.title("Preprocessing")
s = setup(data, target = 'medv', session_id=123, silent=True, html=False)
'preprocess', s[0]
#st.title('Baseline Models')
lr = create_model('lr')
s[-2][0]
#best = regression.compare_models(blacklist=['catboost'])
#regression.plot_model(best)
#st.pyplot()
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setup.py
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<<<<<<< HEAD
<<<<<<< HEAD
# Copyright (C) 2019-2020 Moez Ali <moez.ali@queensu.ca>
# License: MIT, moez.ali@queensu.ca
from setuptools import setup
def readme():
with open('README.md') as f:
README = f.read()
return README
setup(
name="pycaret",
version="1.0.0",
description="An open source, low-code machine learning library in Python",
long_description=readme(),
long_description_content_type="text/markdown",
url="https://github.com/pycaret/pycaret",
author="Moez Ali",
author_email="moez.ali@queensu.ca",
license="MIT",
classifiers=[
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
],
packages=["pycaret"],
include_package_data=True,
install_requires=["pandas", "numpy", "seaborn", "matplotlib", "IPython", "joblib",
"scikit-learn==0.22", "shap==0.32.1", "ipywidgets", "yellowbrick==1.0.1", "xgboost==0.90",
"wordcloud", "textblob", "plotly==4.4.1", "cufflinks==0.17.0", "umap-learn",
"lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk", "mlxtend",
"pyod", "catboost==0.20.2", "pandas-profiling==2.3.0", "kmodes==0.10.1",
"datefinder==0.7.0", "datetime", "DateTime==4.3", "awscli"]
)
=======
=======
>>>>>>> dev-1.0.1
# Copyright (C) 2019-2020 Moez Ali <moez.ali@queensu.ca>
# License: MIT, moez.ali@queensu.ca
......@@ -69,8 +30,4 @@ setup(
packages=["pycaret"],
include_package_data=True,
install_requires=required
)
<<<<<<< HEAD
>>>>>>> dev-1.0.1
=======
>>>>>>> dev-1.0.1
)
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