From 6794647d897b8ac7d43e7c9efe71c381c5dfd0f6 Mon Sep 17 00:00:00 2001 From: adnankarol Date: Thu, 1 Aug 2024 19:46:16 +0200 Subject: [PATCH] Restructure --- __init__.py | 0 .../classifierAgent.py => classifierAgent.py | 2 +- classifierAgent/__init__.py | 1 - classifierAgent/cli.py | 14 - setup.py | 16 +- test_package.ipynb | 400 +++++++++++++++++- 6 files changed, 397 insertions(+), 36 deletions(-) create mode 100644 __init__.py rename classifierAgent/classifierAgent.py => classifierAgent.py (99%) delete mode 100644 classifierAgent/__init__.py delete mode 100644 classifierAgent/cli.py diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/classifierAgent/classifierAgent.py b/classifierAgent.py similarity index 99% rename from classifierAgent/classifierAgent.py rename to classifierAgent.py index 349bdc6..d5131e0 100644 --- a/classifierAgent/classifierAgent.py +++ b/classifierAgent.py @@ -1,5 +1,5 @@ __author__ = "Adnan Karol" -__version__ = "1.0.4" +__version__ = "1.0.7" __maintainer__ = "Adnan Karol" __email__ = "adnanmushtaq5@gmail.com" __status__ = "PROD" diff --git a/classifierAgent/__init__.py b/classifierAgent/__init__.py deleted file mode 100644 index 9519c59..0000000 --- a/classifierAgent/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from classifierAgent import classifierAgent \ No newline at end of file diff --git a/classifierAgent/cli.py b/classifierAgent/cli.py deleted file mode 100644 index f5162b0..0000000 --- a/classifierAgent/cli.py +++ /dev/null @@ -1,14 +0,0 @@ -# classifierAgent/cli.py -__author__ = "Adnan Karol" -__version__ = "1.0.4" -__maintainer__ = "Adnan Karol" -__email__ = "adnanmushtaq5@gmail.com" -__status__ = "PROD" - -from classifierAgent import classifierAgent - -def main(): - classifierAgent() # Call your function here - -if __name__ == "__main__": - main() diff --git a/setup.py b/setup.py index 13a2524..fcd5a5d 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ __author__ = "Adnan Karol" -__version__ = "1.0.4" +__version__ = "1.0.7" __maintainer__ = "Adnan Karol" __email__ = "adnanmushtaq5@gmail.com" __status__ = "PROD" @@ -18,7 +18,7 @@ def parse_requirements(filename): setup( name='classifierAgent', - version='1.0.4', # Updated version number + version='1.0.7', # Updated version number description='A Python package for performing classification on datasets in CSV or Excel format.', long_description=long_description, long_description_content_type="text/markdown", @@ -37,15 +37,5 @@ def parse_requirements(filename): ], python_requires='>=3.10', packages=find_packages(), # Automatically find packages in the directory - install_requires=parse_requirements('requirements.txt'), - entry_points={ - 'console_scripts': [ - 'classifierAgent=classifierAgent.cli:main', - ], - }, - project_urls={ - 'Documentation': 'https://github.com/adnanmushtaq1996/ML-Classifier-Python-Package', - 'Source': 'https://github.com/adnanmushtaq1996/ML-Classifier-Python-Package', - 'Tracker': 'https://github.com/adnanmushtaq1996/ML-Classifier-Python-Package/issues', - }, + install_requires=parse_requirements('requirements.txt') ) diff --git a/test_package.ipynb b/test_package.ipynb index 8f5bab3..3014bd6 100644 --- a/test_package.ipynb +++ b/test_package.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,9 +39,272 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32mDataset loaded successfully!\u001b[0m\n", + "\u001b[36mDataset split into features and target: target\u001b[0m\n", + "\u001b[36mTraining and testing sets created.\u001b[0m\n", + "\u001b[36mData scaled using minmax method.\u001b[0m\n", + "\u001b[33mTraining on Model: KNeighborsClassifier\u001b[0m\n", + "\u001b[32mTraining on Model: KNeighborsClassifier complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for KNeighborsClassifier:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.71 0.84 0.77 115\n", + " 1 0.84 0.71 0.77 135\n", + "\n", + " accuracy 0.77 250\n", + " macro avg 0.78 0.78 0.77 250\n", + "weighted avg 0.78 0.77 0.77 250\n", + "\u001b[0m\n", + "\u001b[32mModel KNeighborsClassifier saved as models/KNeighborsClassifier_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: LogisticRegression\u001b[0m\n", + "\u001b[32mTraining on Model: LogisticRegression complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for LogisticRegression:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.79 0.90 0.85 115\n", + " 1 0.91 0.80 0.85 135\n", + "\n", + " accuracy 0.85 250\n", + " macro avg 0.85 0.85 0.85 250\n", + "weighted avg 0.86 0.85 0.85 250\n", + "\u001b[0m\n", + "\u001b[32mModel LogisticRegression saved as models/LogisticRegression_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: DecisionTreeClassifier\u001b[0m\n", + "\u001b[32mTraining on Model: DecisionTreeClassifier complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for DecisionTreeClassifier:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.88 0.85 115\n", + " 1 0.89 0.84 0.86 135\n", + "\n", + " accuracy 0.86 250\n", + " macro avg 0.86 0.86 0.86 250\n", + "weighted avg 0.86 0.86 0.86 250\n", + "\u001b[0m\n", + "\u001b[32mModel DecisionTreeClassifier saved as models/DecisionTreeClassifier_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: RandomForestClassifier\u001b[0m\n", + "\u001b[32mTraining on Model: RandomForestClassifier complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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RbMOyYmJinI7dtm3bHEM/T506pT/++EN//PGHzp8/r0aNGumrr75yvAeDgoK0efPmG5o97ty5c7n+TLqWK+tOSkrS2bNnVbduXaf3Tm4+O4KCgvTLL7/ou++++1v1XMv8+fNVt25dFS1a1HE8//jjDzVu3FgZGRn66quvnPrn9T0CIG8IRoDBAgICJF2+HiA3jh49Kg8PD0VGRjq1h4SEKCgoSEePHnVqv+OOO7Jto2jRokpKSrrBirN74oknFB0drW7duum2225Thw4d9Mknn1w3JGXVWaFChWzLKlWq5Phj70pXP5es4Vp5eS4tWrRQkSJFNG/ePCUkJOjee+/NdiyzZGZmavz48brzzjtlt9tVokQJlSxZUjt27NDZs2dzvc8yZcrk6eLssWPHqlixYtq2bZsmTZqk4ODgXK97taNHj+rOO+/MdsF+pUqVHMuvFB4enqft33fffVq1apW++OILjR07VkFBQUpKSsr2fI8dO+YIJ1nXDdWvX1+Ssh1Lb2/vbH94Xv2aPXr0qEqVKpVt2OfVr6eb8ToLDAyUt7e3SpQoka09p9fenXfeqcaNGzv9ZL1Wjx49es1arqw3y9W/j/3790u6HJiyAmnWz4wZM5Samuo4nqNHj9aPP/6osmXLqlatWhoyZEiuvxAJCAjI9WfStSxdulT333+/vL29VaxYMZUsWVJTpkxx+n3n5rNjwIAB8vf3V61atXTnnXeqd+/efzlMNy/279+vL774ItvxbNy4saTL1+hdKa/vEQB5wzVGgMECAgJUunRp/fjjj3la7+rJD67l6pmasliWdcP7uPoieB8fH3311Vdau3atli1bpi+++ELz5s3Tgw8+qJUrV16zhrz6O88li91uV7t27TR79mwdOnRIQ4YMuWbfkSNHavDgwXrmmWc0fPhwFStWTB4eHnrppZdyfWZMcv7mPDe2bt3q+GNs586dTmd78lteay1RooTjD8imTZuqYsWKatWqlSZOnOg4q5mRkaEmTZro9OnTGjBggCpWrCg/Pz8dP35csbGx2Y7lzXq93Kic9n8zXns34urfR9axGjNmzDWnhs8Ki+3bt1fdunX12WefaeXKlRozZoxGjRqlTz/9VM2bN7/ufitWrKitW7fq559/VtmyZfNc94YNG/Twww+rXr16euedd1SqVCkVLlxYs2bNcpo0ITefHZUqVdLevXu1dOlSffHFF1q4cKHeeecdvfbaaxo6dGiea7taZmammjRpoldeeSXH5eXLl3d6nNf3CIC8IRgBhmvVqpXeffddbdq0SbVr175u39DQUGVmZmr//v2Ob5kl6bffftOZM2ccM8zdDEWLFs1xlq+rv9WWJA8PDzVq1EiNGjXSW2+9pZEjR+rVV1/V2rVrHX84X/08pMv3FLnaTz/9pBIlSsjPz+/vP4kcdOzYUe+99548PDzUoUOHa/ZbsGCBGjZsqJkzZzq1nzlzxunsQW5Dam6cP39eXbp0UVRUlOrUqaPRo0erbdu2jpnv8io0NFQ7duxQZmam01mjn376ybH8ZmrZsqXq16+vkSNH6tlnn5Wfn5927typffv2afbs2ercubOj76pVq254P6Ghofryyy+VnJzsdNbo6teTK19nOQkNDb1mLVnLryciIkLS5S9UcnpfXa1UqVJ67rnn9Nxzz+nkyZOqUaOGRowY4QhG13rttm7dWh999JE++OADDRo06C/3c7WFCxfK29tbK1ascJoee9asWdn65uazw8/PT0888YSeeOIJpaWlqV27dhoxYoQGDRr0t28DEBERoeTk5FwdTwD5j6F0gOFeeeUV+fn5qVu3bvrtt9+yLT948KAmTpwoSY4bR149U9Jbb70lSY7Znm6GiIgInT17Vjt27HC0JSYmZpv57vTp09nWzfo2++opxLOUKlVK1apV0+zZs53C148//qiVK1dmu0HmzdSwYUMNHz5cb7/9tkJCQq7Zz9PTM9sZgfnz5+v48eNObVl/WF9rqui8GDBggI4dO6bZs2frrbfeUlhYmOOGqTeiRYsW+vXXXzVv3jxHW3p6uiZPnix/f3/HcLabacCAATp16pSmT58u6f/Otlx5LC3Lcrymb0SLFi2Unp6uKVOmONoyMjI0efJkp36ufJ3lpEWLFvr222+1adMmR9v58+f17rvvKiws7C/vQ1WzZk1FRERo7NixSk5OzrY8655kGRkZ2YYoBgcHq3Tp0k6vJT8/vxyHhT722GOqUqWKRowY4VRrlj///FOvvvrqNev09PSUzWZzOrt85MgRLVq0yKlfbj47Tp065bTcy8tLUVFRsixLly5dumYNudW+fXtt2rRJK1asyLbszJkzSk9P/9v7AJB7nDECDBcREaEPP/zQMU1w586dVblyZaWlpembb75xTK8sSVWrVlVMTIzeffddnTlzRvXr19e3336r2bNn65FHHlHDhg1vWl0dOnTQgAED1LZtW/Xp00cpKSmaMmWKypcv73QB9bBhw/TVV1+pZcuWCg0N1cmTJ/XOO+/o9ttvz/EeKFnGjBmj5s2bq3bt2uratatjGuXAwMDrDnH7uzw8PPTvf//7L/u1atVKw4YNU5cuXVSnTh3t3LlTCQkJKleunFO/iIgIBQUFaerUqSpSpIj8/Px033335flahDVr1uidd97R66+/7pg+fNasWWrQoIEGDx6s0aNH52l70uWJAKZNm6bY2Fj98MMPCgsL04IFC/T1119rwoQJf/sC+5w0b95clStX1ltvvaXevXurYsWKioiIUFxcnI4fP66AgAAtXLjwb13n1rp1a0VHR2vgwIE6cuSIoqKi9Omnn+b4R76rXmc5GThwoD766CM1b95cffr0UbFixTR79mwdPnxYCxcu/Mubt3p4eGjGjBlq3ry57rrrLnXp0kVlypTR8ePHtXbtWgUEBGjJkiX6888/dfvtt+uxxx5T1apV5e/vr9WrV+u7775zuv9SzZo1NW/ePL388su699575e/vr9atW6tw4cL69NNP1bhxY9WrV0/t27dXdHS0ChcurF27dunDDz9U0aJFr3kvo5YtW+qtt95Ss2bN1LFjR508eVL/+c9/FBkZ6fRFS24+Ox566CGFhIQoOjpat912m/bs2aO3335bLVu2vCmv3/79+2vx4sVq1aqV41YG58+f186dO7VgwQIdOXIk2/VlAPKRy+bDA+BW9u3bZ3Xv3t0KCwuzvLy8rCJFiljR0dHW5MmTrYsXLzr6Xbp0yRo6dKgVHh5uFS5c2Cpbtqw1aNAgpz6WlfP0zZaVfZroa03XbVmWtXLlSqty5cqWl5eXVaFCBeuDDz7INl33l19+abVp08YqXbq05eXlZZUuXdp68sknnaa/zWm6bsuyrNWrV1vR0dGWj4+PFRAQYLVu3dravXu3U5+s/V09pW9OU/Tm5FrT9l7pWtN19+vXzypVqpTl4+NjRUdHW5s2bcpxmu3//ve/VlRUlFWoUCGn51m/fv1rTjV85XbOnTtnhYaGWjVq1LAuXbrk1K9v376Wh4eHtWnTpus+h2v9vn/77TerS5cuVokSJSwvLy+rSpUq2X4P13sN5HV/lmVZ77//vtNx2L17t9W4cWPL39/fKlGihNW9e3dr+/bt2V4T1/pdXf2asyzLOnXqlPX0009bAQEBVmBgoPX0009bW7duvemvs2vVdPXvNrfH8ODBg9Zjjz1mBQUFWd7e3latWrWspUuXOvXJmq57/vz5OW5j69atVrt27azixYtbdrvdCg0Ntdq3b299+eWXlmVZVmpqqtW/f3+ratWqVpEiRSw/Pz+ratWq1jvvvOO0neTkZKtjx45WUFCQJSnblNtJSUnWa6+9ZlWpUsXy9fW1vL29rcqVK1uDBg2yEhMTnY7R1evOnDnTuvPOOy273W5VrFjRmjVr1g19dkybNs2qV6+e47lGRERY/fv3t86ePevo83em67asy1OmDxo0yIqMjLS8vLysEiVKWHXq1LHGjh1rpaWlWZZ1Y+8RAHlns6x8vnoTAAAAANwc1xgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeIVcXUB+8GkyytUlAADcQNLnA1xdAgDAxbxzmXg4YwQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxnObYLRhwwZ16tRJtWvX1vHjxyVJc+fO1caNG11cGQAAAIBbnVsEo4ULF6pp06by8fHR1q1blZqaKkk6e/asRo4c6eLqAAAAANzq3CIYvfHGG5o6daqmT5+uwoULO9qjo6O1ZcsWF1YGAAAAwARuEYz27t2revXqZWsPDAzUmTNnCr4gAAAAAEZxi2AUEhKiAwcOZGvfuHGjypUr54KKAAAAAJjELYJR9+7d9eKLL2rz5s2y2Ww6ceKEEhISFBcXp169erm6PAAAAAC3uEKuLkCSBg4cqMzMTDVq1EgpKSmqV6+e7Ha74uLi9MILL7i6PAAAAAC3OJtlWZari8iSlpamAwcOKDk5WVFRUfL397+h7fg0GXWTKwMA/BMlfT7A1SUAAFzMO5engtxiKN0HH3yglJQUeXl5KSoqSrVq1brhUAQAAAAAeeUWwahv374KDg5Wx44dtXz5cmVkZLi6JAAAAAAGcYtglJiYqI8//lg2m03t27dXqVKl1Lt3b33zzTeuLg0AAACAAdwiGBUqVEitWrVSQkKCTp48qfHjx+vIkSNq2LChIiIiXF0eAAAAgFucW8xKdyVfX181bdpUSUlJOnr0qPbs2ePqkgAAAADc4tzijJEkpaSkKCEhQS1atFCZMmU0YcIEtW3bVrt27XJ1aQAAAABucW5xxqhDhw5aunSpfH191b59ew0ePFi1a9d2dVkAAAAADOEWwcjT01OffPKJmjZtKk9PT1eXAwAAAMAwbhGMEhISXF0CAAAAAIO5LBhNmjRJPXr0kLe3tyZNmnTdvn369CmgqgAAAACYyGZZluWKHYeHh+v7779X8eLFFR4efs1+NptNhw4dytO2fZqM+rvlAQBuAUmfD3B1CQAAF/PO5akgl50xOnz4cI7/BgAAAICC5hbTdQ8bNkwpKSnZ2i9cuKBhw4a5oCIAAAAAJnHZULoreXp6KjExUcHBwU7tp06dUnBwsDIyMvK0PYbSAQAkhtIBAP4BQ+muZFmWbDZbtvbt27erWLFiLqgIcD/RVW5X38fvU43yt6lU8SJq//qnWvLNfqc+g2MeUJfmVRXkb9emXcfVZ9JKHTyeJEmqe3dZrRzXMcdtP9B7tn7Y92u+PwcAQP7LyMjQlP9M1rKli3Xqjz9UMjhYD7dpqx49n8vx7y0Al7k0GBUtWlQ2m002m03ly5d3erNmZGQoOTlZPXv2dGGFgPvw8/bSzkMnNWfFDs0b0i7b8n5P3KfnHqmp7qOX6civZ/VabF0tiW+v6l1nKPVShv63+7jC2r/ttM5rsXXVsHoooQgAbiGzZk7X/HkfafjIUYqIjNTuH3/Ua/8eJP8iRfRUp86uLg9wWy4NRhMmTJBlWXrmmWc0dOhQBQYGOpZ5eXkpLCxMtWvXdmGFgPtY+d0hrfzu2jM09m57j0YlbNLSTQckSd1GLdXR+S/o4ejymr9ujy6lZ+q3pPOO/oU8PdSqdqSm/HdLvtcOACg427ZtVYMHG6le/QaSpDJlbtfny5fpx507XFsY4OZcGoxiYmIkXZ66u06dOipcuLArywH+scJCAlWquL/WbD3iaDuXkqbvfjqh+6JKa/66PdnWaVU7UsUDfDR3xc4CrBQAkN+qVauuhfM/0ZEjhxUWFq69P/2krVt/UNwrA11dGuDW3OIao/r16zv+ffHiRaWlpTktDwgIuOa6qampSk1NdWqzMtNl83CLpwYUiJBi/pKkk1ecEbr8OEW3FfXLcZ2Y5ndr1Q+HdfyPP/O9PgBAwXmmWw8lJyfrkVbN5enpqYyMDL3wYl+1bPWwq0sD3JpbTNedkpKi559/XsHBwfLz81PRokWdfq4nPj5egYGBTj/ph9cWUOXAP1OZEkXUpGa4Zn/OsAoAuNWs+OJzLV+2RPGjx+nj+Z9q+Mg3NXvWe1q86DNXlwa4NbcIRv3799eaNWs0ZcoU2e12zZgxQ0OHDlXp0qU1Z86c6647aNAgnT171umnUHjDAqoccA+/nk6WJAVfdXYouKiv03VFWZ5uWkWnzl1wXI8EALh1jB83Ws907aHmLVrqzvIV1PrhR9Spc4xmzpjm6tIAt+YW482WLFmiOXPmqEGDBurSpYvq1q2ryMhIhYaGKiEhQU899dQ117Xb7bLb7U5tDKODaY78elaJp5LVsHqodhw8KUkq4uuleyuW1vQl27L179y0ij5cvUvpGZkFXCkAIL9dvHBRHh7O03J7enoqM9Plt64E3JpbJIjTp0+rXLlyki5fT3T69GlJ0gMPPKBevXq5sjTAbfh5F1ZEmf8bWhoWEqi7I4KVdO6Cfv79T/3ns+81oGMdHTiepCOJZ/R6bF0lnkrW4q/3OW2nQfVQhZcK0qzPtxf0UwAAFID6DRpq+rtTFVKqtCIiI/XTnj2aO3uW2rR91NWlAW7NLYJRuXLldPjwYd1xxx2qWLGiPvnkE9WqVUtLlixRUFCQq8sD3EKN8iFON2gd3auRJGnuyp3qMWa5xs3bLF/vwnr7paYK8vfWNz/+oocHfaLUSxlO24ltdrc27fpF+34+XaD1AwAKxsBX/63/TJqokcOH6vTpUyoZHKzHHn9Cz/bq7erSALdmsyzL5edVx48fL09PT/Xp00erV69W69atZVmWLl26pLfeeksvvvhinrbn02RUPlUKAPgnSfp8gKtLAAC4mHcuTwW5RTC62tGjR/XDDz8oMjJSd999d57XJxgBACSCEQAg98HILYbSXS00NFShoaGuLgMAAACAIdwiGE2aNCnHdpvNJm9vb0VGRqpevXry9PQs4MoAAAAAmMAtgtH48eP1+++/KyUlxXFD16SkJPn6+srf318nT55UuXLltHbtWpUtW9bF1QIAAAC41bjFDV5Hjhype++9V/v379epU6d06tQp7du3T/fdd58mTpyoY8eOKSQkRH379nV1qQAAAABuQW4x+UJERIQWLlyoatWqObVv3bpVjz76qA4dOqRvvvlGjz76qBITE/9ye0y+AACQmHwBAJD7yRfc4oxRYmKi0tPTs7Wnp6fr119/lSSVLl1af/75Z0GXBgAAAMAAbhGMGjZsqGeffVZbt251tG3dulW9evXSgw8+KEnauXOnwsPDXVUiAAAAgFuYWwSjmTNnqlixYqpZs6bsdrvsdrvuueceFStWTDNnzpQk+fv7a9y4cS6uFAAAAMCtyC2uMcry008/ad++fZKkChUqqEKFCje0Ha4xAgBIXGMEAPiH3uC1XLlystlsioiIUKFCblUaAAAAgFuYWwylS0lJUdeuXeXr66u77rpLx44dkyS98MILevPNN11cHQAAAIBbnVsEo0GDBmn79u1at26dvL29He2NGzfWvHnzXFgZAAAAABO4xXi1RYsWad68ebr//vtls9kc7XfddZcOHjzowsoAAAAAmMAtzhj9/vvvCg4OztZ+/vx5p6AEAAAAAPnBLYLRPffco2XLljkeZ4WhGTNmqHbt2q4qCwAAAIAh3GIo3ciRI9W8eXPt3r1b6enpmjhxonbv3q1vvvlG69evd3V5AAAAAG5xbnHG6IEHHtC2bduUnp6uKlWqaOXKlQoODtamTZtUs2ZNV5cHAAAA4BbnFmeMJCkiIkLTp093dRkAAAAADOTSYOTh4fGXkyvYbDalp6cXUEUAAAAATOTSYPTZZ59dc9mmTZs0adIkZWZmFmBFAAAAAEzk0mDUpk2bbG179+7VwIEDtWTJEj311FMaNmyYCyoDAAAAYBK3mHxBkk6cOKHu3burSpUqSk9P17Zt2zR79myFhoa6ujQAAAAAtziXB6OzZ89qwIABioyM1K5du/Tll19qyZIlqly5sqtLAwAAAGAIlw6lGz16tEaNGqWQkBB99NFHOQ6tAwAAAID8ZrMsy3LVzj08POTj46PGjRvL09Pzmv0+/fTTPG3Xp8mov1saAOAWkPT5AFeXAABwMe9cngpy6Rmjzp07/+V03QAAAACQ31wajN5//31X7h4AAAAAJLnB5AsAAAAA4GoEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4NxSMNmzYoE6dOql27do6fvy4JGnu3LnauHHjTS0OAAAAAApCnoPRwoUL1bRpU/n4+Gjr1q1KTU2VJJ09e1YjR4686QUCAAAAQH7LczB64403NHXqVE2fPl2FCxd2tEdHR2vLli03tTgAAAAAKAh5DkZ79+5VvXr1srUHBgbqzJkzN6MmAAAAAChQeQ5GISEhOnDgQLb2jRs3qly5cjelKAAAAAAoSHkORt27d9eLL76ozZs3y2az6cSJE0pISFBcXJx69eqVHzUCAAAAQL4qlNcVBg4cqMzMTDVq1EgpKSmqV6+e7Ha74uLi9MILL+RHjQAAAACQr2yWZVk3smJaWpoOHDig5ORkRUVFyd/f/2bXdsN8moxydQkAADeQ9PkAV5cAAHAx71yeCsrzGaMsXl5eioqKutHVAQAAAMBt5DkYNWzYUDab7ZrL16xZ87cKAgAAAICCludgVK1aNafHly5d0rZt2/Tjjz8qJibmZtUFAAAAAAUmz8Fo/PjxObYPGTJEycnJf7sgAAAAAChoNzz5wtUOHDigWrVq6fTp0zdjc3/LsdOpri4BAOAGKjTq5+oSAAAudmHr27nql+f7GF3Lpk2b5O3tfbM2BwAAAAAFJs9D6dq1a+f02LIsJSYm6vvvv9fgwYNvWmEAAAAAUFDyHIwCAwOdHnt4eKhChQoaNmyYHnrooZtWGAAAAAAUlDwFo4yMDHXp0kVVqlRR0aJF86smAAAAAChQebrGyNPTUw899JDOnDmTT+UAAAAAQMHL8+QLlStX1qFDh/KjFgAAAABwiTwHozfeeENxcXFaunSpEhMTde7cOacfAAAAAPinyfU1RsOGDVO/fv3UokULSdLDDz8sm83mWG5Zlmw2mzIyMm5+lQAAAACQj3J9g1dPT08lJiZqz5491+1Xv379m1LY38ENXgEAEjd4BQDk/gavuT5jlJWf3CH4AAAAAMDNlKdrjK4cOgcAAAAAt4o83ceofPnyfxmOTp8+/bcKAgAAAICClqdgNHToUAUGBuZXLQAAAADgEnkKRh06dFBwcHB+1QIAAAAALpHra4y4vggAAADArSrXwSiXs3oDAAAAwD9OrofSZWZm5mcdAAAAAOAyeZquGwAAAABuRQQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8twlGGzZsUKdOnVS7dm0dP35ckjR37lxt3LjRxZUBAAAAuNW5RTBauHChmjZtKh8fH23dulWpqamSpLNnz2rkyJEurg4AAADArc4tgtEbb7yhqVOnavr06SpcuLCjPTo6Wlu2bHFhZQAAAABM4BbBaO/evapXr1629sDAQJ05c6bgCwIAAABgFLcIRiEhITpw4EC29o0bN6pcuXIuqAgAAACASdwiGHXv3l0vvviiNm/eLJvNphMnTighIUFxcXHq1auXq8sDAAAAcIsr5OoCJGngwIHKzMxUo0aNlJKSonr16slutysuLk4vvPCCq8sDAAAAcIuzWZZlubqILGlpaTpw4ICSk5MVFRUlf3//G9rOsdOpN7kyAMA/UYVG/VxdAgDAxS5sfTtX/dxiKN0HH3yglJQUeXl5KSoqSrVq1brhUAQAAAAAeeUWwahv374KDg5Wx44dtXz5cmVkZLi6JAAAAAAGcYtglJiYqI8//lg2m03t27dXqVKl1Lt3b33zzTeuLg0AAACAAdwiGBUqVEitWrVSQkKCTp48qfHjx+vIkSNq2LChIiIiXF0eAAAAgFucW8xKdyVfX181bdpUSUlJOnr0qPbs2ePqkgAAAADc4tzijJEkpaSkKCEhQS1atFCZMmU0YcIEtW3bVrt27XJ1aQAAAABucW5xxqhDhw5aunSpfH191b59ew0ePFi1a9d2dVkAAAAADOEWwcjT01OffPKJmjZtKk9PT1eXAwAAAMAwbhGMEhISXF0CAAAAAIO5LBhNmjRJPXr0kLe3tyZNmnTdvn369CmgqgAAAACYyGZZluWKHYeHh+v7779X8eLFFR4efs1+NptNhw4dytO2j51O/bvlAW7vo9kztHH9l/r56GHZ7XZFVammbs+9pLKh//d+SktN1dRJY7Vu9Re6dClN99xXR336/1tFixV3YeVAwanQqJ+rSwBuuugaEerbubFqRN2hUiUD1b7vu1qybodjeZsHq6rbYw+oeqU7VDzIT/c9Ea8d+447bWPF9BdV7547ndqmL9ioPiM+LpDnABSkC1vfzlU/l50xOnz4cI7/BpA7O7Z+r4cf7aAKle5SRkaG3ps6SQNf6qkZH34mHx9fSdKUiaO1+ZsNGjxirPz8i+jtcSM1ZGBfTXx3jourBwDcKD8fu3buO645/92keW/1yLbc18dL32w7qIWrtmjKa09dczszF36t4VOWOh6nXLyUL/UC/xRucY3RsGHDFBcXJ19fX6f2CxcuaMyYMXrttddcVBngvuInTHV63P/fw/V4iwba/9Nu3V39Hp1P/lNfLPlMg4a+qer33CdJint1uLo+2Ua7f9yuqMpVXVE2AOBvWvn1bq38evc1l3+07DtJ0h2lil13Oxcupum3U3/e1NqAfzK3uI/R0KFDlZycnK09JSVFQ4cOdUFFwD/P+f//HioSEChJ2vfTbqWnp6vGvfc7+twRFq7gkFLas3NHjtsAAJjjiRb36Oc1b+r7+f/SsBcelo93YVeXBLiUW5wxsixLNpstW/v27dtVrNj1v+1ITU1VamrqVW2S3W6/qTUC7iwzM1NTJozWXXdXV3jE5THjSaf+UOHCheVfJMCpb9GixXX69B+uKBMA4Cbmff69jiWeVuLvZ1XlztJ648U2Kh8arA5xM1xdGuAyLg1GRYsWlc1mk81mU/ny5Z3CUUZGhpKTk9WzZ8/rbiM+Pj7bWaWXXnlVfQcMzpeaAXc0eewIHTl0QOOnve/qUgAA/wDvffq149+7DpxQ4h/n9MW7fRR+ewkd/oUvz2AmlwajCRMmyLIsPfPMMxo6dKgCAwMdy7y8vBQWFqbatWtfdxuDBg3Syy+/7NT22/l8KRdwS5PHjtTmr7/SuCmzVDI4xNFetHgJXbp0Scl/nnM6a5SUdErFipVwRakAADf13c4jkqSIsiUJRjCWS4NRTEyMpMtTd9epU0eFC+d9bKvdbs82bO5MOtN149ZnWZbeHhevr9ev0dh3ZqpU6dudlpevGKVChQpp6/ebVbdhE0nSz0cP6+SviapU5W5XlAwAcFNVK1z+P+TXP866uBLAdVwWjM6dO6eAgMvfYlevXl0XLlzQhQsXcuyb1Q/A/5k8doTWrPxcQ0dNlK+vn06fuvwNn5+fv+ze3vLzL6Jmrdtq6qSxKhIQKF8/f/1nXLyiKldlRjoA+Afz8/FSRNmSjsdhZYrr7vJllHQuRT//mqSiAb4qG1JUpYIvj8QpH3abJOm3U+f026k/FX57CT3R/B6t2LhLp86cV5XyZTS6Xztt+GG/ftx/wiXPCXAHLrvBq6enpxITExUcHCwPD48cJ1/ImpQhIyMjT9vmBq8wQZPaOZ/1ifv3cDVt2UbSFTd4XfW5Ll1KU837otWn/6sqVpyhdDADN3jFrahuzTu1csaL2drnLv6ferz+gTq1vk/Thz2dbfkbU5drxLTluv22IL03IkZREaXl5+OlX35L0uI12/XmjBX68/zFgngKQIHK7Q1eXRaM1q9fr+joaBUqVEjr16+/bt/69evnadsEIwCARDACAOQ+GLlsKN2VYSevwQcAAAAAbia3uMHrF198oY0bNzoe/+c//1G1atXUsWNHJSUlubAyAAAAACZwi2DUv39/nTt3TpK0c+dOvfzyy2rRooUOHz6cbSpuAAAAALjZXDpdd5bDhw8rKipKkrRw4UK1bt1aI0eO1JYtW9SiRQsXVwcAAADgVucWZ4y8vLyUkpIiSVq9erUeeughSVKxYsUcZ5IAAAAAIL+4xRmjBx54QC+//LKio6P17bffat68eZKkffv26fbbb/+LtQEAAADg73GLM0Zvv/22ChUqpAULFmjKlCkqU6aMJOnzzz9Xs2bNXFwdAAAAgFudy+5jlJ+4jxEAQOI+RgCAf8B9jK6WkZGhRYsWac+ePZKku+66Sw8//LA8PT1dXBkAAACAW51bBKMDBw6oRYsWOn78uCpUqCBJio+PV9myZbVs2TJFRES4uEIAAAAAtzK3uMaoT58+ioiI0M8//6wtW7Zoy5YtOnbsmMLDw9WnTx9XlwcAAADgFucWZ4zWr1+v//3vfypWrJijrXjx4nrzzTcVHR3twsoAAAAAmMAtzhjZ7Xb9+eef2dqTk5Pl5eXlgooAAAAAmMQtglGrVq3Uo0cPbd68WZZlybIs/e9//1PPnj318MMPu7o8AAAAALc4twhGkyZNUmRkpOrUqSNvb295e3srOjpakZGRmjhxoqvLAwAAAHCLc+k1RpmZmRozZowWL16stLQ0PfLII4qJiZHNZlOlSpUUGRnpyvIAAAAAGMKlwWjEiBEaMmSIGjduLB8fHy1fvlyBgYF67733XFkWAAAAAMO4dCjdnDlz9M4772jFihVatGiRlixZooSEBGVmZrqyLAAAAACGcWkwOnbsmFq0aOF43LhxY9lsNp04ccKFVQEAAAAwjUuDUXp6ury9vZ3aChcurEuXLrmoIgAAAAAmcuk1RpZlKTY2Vna73dF28eJF9ezZU35+fo62Tz/91BXlAQAAADCES4NRTExMtrZOnTq5oBIAAAAAJnNpMJo1a5Yrdw8AAAAAktzkBq8AAAAA4EoEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAYj2AEAAAAwHgEIwAAAADGIxgBAAAAMB7BCAAAAIDxCEYAAAAAjEcwAgAAAGA8ghEAAAAA4xGMAAAAABiPYAQAAADAeAQjAAAAAMazWZZluboIADdXamqq4uPjNWjQINntdleXAwBwEf4/AHKPYATcgs6dO6fAwECdPXtWAQEBri4HAOAi/H8A5B5D6QAAAAAYj2AEAAAAwHgEIwAAAADGIxgBtyC73a7XX3+dC20BwHD8fwDkHpMvAAAAADAeZ4wAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAVBYWJgmTJjg6jIAADfBunXrZLPZdObMmev247MfcEYwAvJZbGysbDab3nzzTaf2RYsWyWazFWgt77//voKCgrK1f/fdd+rRo0eB1gIApsv6/8Fms8nLy0uRkZEaNmyY0tPT/9Z269Spo8TERAUGBkrisx/ILYIRUAC8vb01atQoJSUlubqUHJUsWVK+vr6uLgMAjNOsWTMlJiZq//796tevn4YMGaIxY8b8rW16eXkpJCTkL79847MfcEYwAgpA48aNFRISovj4+Gv22bhxo+rWrSsfHx+VLVtWffr00fnz5x3LExMT1bJlS/n4+Cg8PFwffvhhtmEQb731lqpUqSI/Pz+VLVtWzz33nJKTkyVdHlrRpUsXnT171vEN5ZAhQyQ5D6fo2LGjnnjiCafaLl26pBIlSmjOnDmSpMzMTMXHxys8PFw+Pj6qWrWqFixYcBOOFACYxW63KyQkRKGhoerVq5caN26sxYsXKykpSZ07d1bRokXl6+ur5s2ba//+/Y71jh49qtatW6to0aLy8/PTXXfdpeXLl0tyHkrHZz+QewQjoAB4enpq5MiRmjx5sn755Zdsyw8ePKhmzZrp0Ucf1Y4dOzRv3jxt3LhRzz//vKNP586ddeLECa1bt04LFy7Uu+++q5MnTzptx8PDQ5MmTdKuXbs0e/ZsrVmzRq+88oqky0MrJkyYoICAACUmJioxMVFxcXHZannqqae0ZMkSR6CSpBUrViglJUVt27aVJMXHx2vOnDmaOnWqdu3apb59+6pTp05av379TTleAGAqHx8fpaWlKTY2Vt9//70WL16sTZs2ybIstWjRQpcuXZIk9e7dW6mpqfrqq6+0c+dOjRo1Sv7+/tm2x2c/kAcWgHwVExNjtWnTxrIsy7r//vutZ555xrIsy/rss8+srLdg165drR49ejitt2HDBsvDw8O6cOGCtWfPHkuS9d133zmW79+/35JkjR8//pr7nj9/vlW8eHHH41mzZlmBgYHZ+oWGhjq2c+nSJatEiRLWnDlzHMuffPJJ64knnrAsy7IuXrxo+fr6Wt98843TNrp27Wo9+eST1z8YAACHK/9/yMzMtFatWmXZ7XbrkUcesSRZX3/9taPvH3/8Yfn4+FiffPKJZVmWVaVKFWvIkCE5bnft2rWWJCspKcmyLD77gdwq5NJUBhhm1KhRevDBB7N9W7d9+3bt2LFDCQkJjjbLspSZmanDhw9r3759KlSokGrUqOFYHhkZqaJFizptZ/Xq1YqPj9dPP/2kc+fOKT09XRcvXlRKSkqux5EXKlRI7du3V0JCgp5++mmdP39e//3vf/Xxxx9Lkg4cOKCUlBQ1adLEab20tDRVr149T8cDAEy3dOlS+fv769KlS8rMzFTHjh3Vrl07LV26VPfdd5+jX/HixVWhQgXt2bNHktSnTx/16tVLK1euVOPGjfXoo4/q7rvvvuE6+OwHJIIRUIDq1aunpk2batCgQYqNjXW0Jycn69lnn1WfPn2yrXPHHXdo3759f7ntI0eOqFWrVurVq5dGjBihYsWKaePGjeratavS0tLydIHtU089pfr16+vkyZNatWqVfHx81KxZM0etkrRs2TKVKVPGaT273Z7rfQAApIYNG2rKlCny8vJS6dKlVahQIS1evPgv1+vWrZuaNm2qZcuWaeXKlYqPj9e4ceP0wgsv3HAtfPbDdAQjoIC9+eabqlatmipUqOBoq1Gjhnbv3q3IyMgc16lQoYLS09O1detW1axZU9Llb++unOXuhx9+UGZmpsaNGycPj8uXD37yySdO2/Hy8lJGRsZf1linTh2VLVtW8+bN0+eff67HH39chQsXliRFRUXJbrfr2LFjql+/ft6ePADAiZ+fX7bP/kqVKik9PV2bN29WnTp1JEmnTp3S3r17FRUV5ehXtmxZ9ezZUz179tSgQYM0ffr0HIMRn/1A7hCMgAJWpUoVPfXUU5o0aZKjbcCAAbr//vv1/PPPq1u3bvLz89Pu3bu1atUqvf3226pYsaIaN26sHj16aMqUKSpcuLD69esnHx8fx3SskZGRunTpkiZPnqzWrVvr66+/1tSpU532HRYWpuTkZH355ZeqWrWqfH19r3kmqWPHjpo6dar27duntWvXOtqLFCmiuLg49e3bV5mZmXrggQd09uxZff311woICFBMTEw+HDUAMMedd96pNm3aqHv37po2bZqKFCmigQMHqkyZMmrTpo0k6aWXXlLz5s1Vvnx5JSUlae3atapUqVKO2+OzH8gdZqUDXGDYsGHKzMx0PL777ru1fv167du3T3Xr1lX16tX12muvqXTp0o4+c+bM0W233aZ69eqpbdu26t69u4oUKSJvb29JUtWqVfXWW29p1KhRqly5shISErJND16nTh317NlTTzzxhEqWLKnRo0dfs8annnpKu3fvVpkyZRQdHe20bPjw4Ro8eLDi4+NVqVIlNWvWTMuWLVN4ePjNODwAYLxZs2apZs2aatWqlWrXri3LsrR8+XLHGZyMjAz17t3b8Rlcvnx5vfPOOzlui89+IHdslmVZri4CQN798ssvKlu2rFavXq1GjRq5uhwAAIB/NIIR8A+xZs0aJScnq0qVKkpMTNQrr7yi48ePa9++fY5vEAEAAHBjuMYI+Ie4dOmS/vWvf+nQoUMqUqSI6tSpo4SEBEIRAADATcAZIwAAAADGY/IFAAAAAMYjGAEAAAAwHsEIAAAAgPEIRgAAAACMRzACAAAAYDyCEQDgHy82NlaPPPKI43GDBg300ksvFXgd69atk81m05kzZwp83wCAv4dgBADIN7GxsbLZbLLZbPLy8lJkZKSGDRum9PT0fN3vp59+quHDh+eqL2EGACBxg1cAQD5r1qyZZs2apdTUVC1fvly9e/dW4cKFNWjQIKd+aWlp8vLyuin7LFas2E3ZDgDAHJwxAgDkK7vdrpCQEIWGhqpXr15q3LixFi9e7Bj+NmLECJUuXVoVKlSQJP38889q3769goKCVKxYMbVp00ZHjhxxbC8jI0Mvv/yygoKCVLx4cb3yyiu6+l7lVw+lS01N1YABA1S2bFnZ7XZFRkZq5syZOnLkiBo2bChJKlq0qGw2m2JjYyVJmZmZio+PV3h4uHx8fFS1alUtWLDAaT/Lly9X+fLl5ePjo4YNGzrVCQD4ZyEYAQAKlI+Pj9LS0iRJX375pfbu3atVq1Zp6dKlunTpkpo2baoiRYpow4YN+vrrr+Xv769mzZo51hk3bpzef/99vffee9q4caNOnz6tzz777Lr77Ny5sz766CNNmjRJe/bs0bRp0+Tv76+yZctq4cKFkqS9e/cqMTFREydOlCTFx8drzpw5mjp1qnbt2qW+ffuqU6dOWr9+vaTLAa5du3Zq3bq1tm3bpm7dumngwIH5ddgAAPmMoXQAgAJhWZa+/PJLrVixQi+88IJ+//13+fn5acaMGY4hdB988IEyMzM1Y8YM2Ww2SdKsWbMUFBSkdevW6aGHHtKECRM0aNAgtWvXTpI0depUrVix4pr73bdvnz755BOtWrVKjRs3liSVK1fOsTxr2F1wcLCCgoIkXT7DNHLkSK1evVq1a9d2rLNx40ZNmzZN9evX15QpUxQREaFx48ZJkipUqKCdO3dq1KhRN/GoAQAKCsEIAJCvli5dKn9/f126dEmZmZnq2LGjhgwZot69e6tKlSpO1xVt375dBw4cUJEiRZy2cfHiRR08eFBnz55VYmKi7rvvPseyQoUK6Z577sk2nC7Ltm3b5Onpqfr16+e65gMHDiglJUVNmjRxak9LS1P16tUlSXv27HGqQ5IjRAEA/nkIRgCAfNWwYUNNmTJFXl5eKl26tAoV+r//evz8/Jz6Jicnq2bNmkpISMi2nZIlS97Q/n18fPK8TnJysiRp2bJlKlOmjNMyu91+Q3UAANwbwQgAkK/8/PwUGRmZq741atTQvHnzFBwcrICAgBz7lCpVSps3b1a9evUkSenp6frhhx9Uo0aNHPtXqVJFmZmZWr9+vWMo3ZWyzlhlZGQ42qKiomS323Xs2LFrnmmqVKmSFi9e7NT2v//976+fJADALTH5AgDAbTz11FMqUaKE2rRpow0bNujw4cNat26d+vTpo19++UWS9OKLL+rNN9/UokWL9NNPP+m555677j2IwsLCFBMTo2eeeUaLFi1ybPOTTz6RJIWGhspms2np0qX6/ffflZycrCJFiiguLk59+/bV7NmzdfDgQW3ZskWTJ0/W7NmzJUk9e/bU/v371b9/f+3du1cffvih3n///fw+RACAfEIwAgC4DV9fX3311Ve644471K5dO1WqVEldu3bVxYsXHWeQ+vXrp6effloxMTGqXbu2ihQporZt2153u1OmTNFjjz2m5557ThUrVlT37t11/vx5SVKZMmU0dOhQDRw4ULfddpuef/55SdLw4cM1ePBgxcfHq1KlSmrWrJmWLVum8PBwSdIdd9yhhQsXatGiRapataqmTp2qkSNH5uPRAQDkJ5t1ratVAQAAAMAQnDECAAAAYDyCEQAAAADjEYwAAAAAGI9gBAAAAMB4BCMAAAAAxiMYAQAAADAewQgAAACA8QhGAAAAAIxHMAIAAABgPIIRAAAAAOMRjAAAAAAY7/8BkBLHMDpsWIAAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for RandomForestClassifier:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.93 0.88 115\n", + " 1 0.93 0.85 0.89 135\n", + "\n", + " accuracy 0.89 250\n", + " macro avg 0.89 0.89 0.89 250\n", + "weighted avg 0.89 0.89 0.89 250\n", + "\u001b[0m\n", + "\u001b[32mModel RandomForestClassifier saved as models/RandomForestClassifier_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: GradientBoostingClassifier\u001b[0m\n", + "\u001b[32mTraining on Model: GradientBoostingClassifier complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for GradientBoostingClassifier:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.91 0.88 115\n", + " 1 0.92 0.85 0.88 135\n", + "\n", + " accuracy 0.88 250\n", + " macro avg 0.88 0.88 0.88 250\n", + "weighted avg 0.88 0.88 0.88 250\n", + "\u001b[0m\n", + "\u001b[32mModel GradientBoostingClassifier saved as models/GradientBoostingClassifier_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: SVC\u001b[0m\n", + "\u001b[32mTraining on Model: SVC complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for SVC:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.90 0.86 115\n", + " 1 0.91 0.83 0.87 135\n", + "\n", + " accuracy 0.86 250\n", + " macro avg 0.86 0.87 0.86 250\n", + "weighted avg 0.87 0.86 0.86 250\n", + "\u001b[0m\n", + "\u001b[32mModel SVC saved as models/SVC_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: GaussianNB\u001b[0m\n", + "\u001b[32mTraining on Model: GaussianNB complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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792698cYbWrVqlVavXn3VbV+ac+/evVlu++WXX1S0aFH5+fn9sydwDR07dtTWrVv1119/qUOHDtdc78svv1TDhg318ccfq0OHDmrSpIkaN26c5TXJbqRmx99//61u3bopKipKvXv31ltvvaXNmzff9PbCwsK0f//+LL/M//LLL47bb0bz5s3l6emp2NjYbN9n/vz58vHx0bJly9S9e3c1b95cjRs3zrLepb0tV77OV/u+k6QSJUqoX79+WrBggQ4dOqQiRYrojTfecFqnatWqevnll7Vu3TqtX79ex44d09SpU685a3a/9v/E5XuNvvnmmxzdNz09XVLWPXYAQBgBuKU9//zz8vPzU8+ePfXHH39kuf3AgQOaMGGCJDne6HH8+PFO64wbN06SburKXtdSvnx5JSYmaseOHY5lCQkJWa58d/r06Sz3vfRGp1deQvySEiVKqHr16po5c6bTL5s///yzli9f7nieeaFhw4Z67bXXNHnyZIWGhl5zPU9Pzyx/yf/iiy907Ngxp2WXAi43fml+4YUXdPToUc2cOVPjxo1TeHi4oqOjr/k63kiLFi30+++/a+7cuY5l6enpmjRpkvz9/dWgQYOb2m7ZsmXVvXt3ffvtt5o8efJV17nytfP09JTNZnPa83P48OEsh5IVKlRIRYsWzbI35IMPPnD6PCMjI8thbSEhISpZsqTj9Tp37pwjIi6pWrWqPDw8rvuaZvdr/0917txZkZGRGjVqVI7ut2jRIklStWrVcnUeALc+LtcN4JZWvnx5ffbZZ3ryySdVqVIldenSRVWqVFFqaqq+//57x+WVpYu/CEVHR+vDDz/U2bNn1aBBA/3vf//TzJkz9dhjj6lhw4a5NleHDh30wgsvqE2bNho0aJCSk5M1ZcoUVahQwekE9JiYGK1bt06PPPKIwsLCdOLECX3wwQcqXbq0HnjggWtu/+2331bz5s1Vu3Zt9ejRw3G57sDAQKf3qsltl95L5kZatmypmJgYdevWTXXq1NHOnTsVGxurcuXKOa1Xvnx5BQUFaerUqQoICJCfn59q1aqV7fN1Llm1apU++OADvfrqq47Lh8+YMUMPPvigXnnlFb311ls52p508cT9adOmqWvXrvrpp58UHh6uL7/8Uhs2bND48eOzfdGPqxk/frwOHTqkgQMH6vPPP1erVq0UEhKiP//8Uxs2bNCiRYucziF75JFHNG7cODVr1kwdO3bUiRMn9P777ysyMtIpvqWLl6R/88031bNnT91zzz1at26d9u3b57TOX3/9pdKlS+vxxx9XtWrV5O/vr5UrV2rz5s169913JV18TQcMGKAnnnhCFSpUUHp6umbPni1PT0+1a9fums8tu1/7f8rT01MvvfTSdS/OsWXLFs2ZM8fxnL/77jvNnz9fderUuep5XAAM57Lr4QFALtq3b5/Vq1cvKzw83PL29rYCAgKsunXrWpMmTbIuXLjgWC8tLc0aNWqUFRERYRUoUMAqU6aMNWLECKd1LOvql2+2rKyXib7W5boty7KWL19uValSxfL29rYqVqxozZkzJ8vlur/77jurdevWVsmSJS1vb2+rZMmS1lNPPWXt27cvy2NceQnmlStXWnXr1rV8fX2tQoUKWa1atbJ2797ttM6lx7vycuAzZsywJFmHDh265mtqWc6XSb6Wa12ue+jQoVaJEiUsX19fq27dutbGjRuvepntb775xoqKirK8vLycnmeDBg2sypUrX/UxL9/OuXPnrLCwMKtGjRpWWlqa03pDhgyxPDw8rI0bN173OVzr6/3HH39Y3bp1s4oWLWp5e3tbVatWzfJ1uN73wPWkp6dbM2bMsB566CErODjY8vLysooWLWo1atTImjp1qnX+/Hmn9T/++GPrjjvusOx2u3XnnXdaM2bMyPL9ZFkXL+vdo0cPKzAw0AoICLDat29vnThxwuly3SkpKdbw4cOtatWqWQEBAZafn59VrVo164MPPnBs5+DBg1b37t2t8uXLWz4+PlZwcLDVsGFDa+XKlVleuysv152dr/2ly3VfeTnwq32/X+v7MC0tzSpfvny2Ltft5eVllStXzho+fLj1119/XfdrA8BMNsvK4VmLAAAAAHCb4RwjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPC9XD5AXfO8e4OoRAABu4Mzmya4eAQDgYj7ZLB72GAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4bhNG69evV+fOnVW7dm0dO3ZMkjR79mzFxcW5eDIAAAAAtzu3CKP58+eradOm8vX11datW5WSkiJJSkxM1OjRo108HQAAAIDbnVuE0euvv66pU6fqo48+UoECBRzL69atqy1btrhwMgAAAAAmcIsw2rt3r+rXr59leWBgoM6ePZv/AwEAAAAwiluEUWhoqOLj47Msj4uLU7ly5VwwEQAAAACTuEUY9erVS4MHD9amTZtks9l0/PhxxcbGatiwYerbt6+rxwMAAABwm/Ny9QCS9OKLLyozM1ONGjVScnKy6tevL7vdrmHDhmngwIGuHg8AAADAbc5mWZbl6iEuSU1NVXx8vJKSkhQVFSV/f/+b2o7v3QNyeTIAwK3ozObJrh4BAOBiPtncFeQWh9LNmTNHycnJ8vb2VlRUlO67776bjiIAAAAAyCm3CKMhQ4YoJCREHTt21NKlS5WRkeHqkQAAAAAYxC3CKCEhQZ9//rlsNpvat2+vEiVKqH///vr+++9dPRoAAAAAA7jVOUaSlJycrK+//lqfffaZVq5cqdKlS+vAgQM52gbnGAEAJM4xAgBk/xwjt7gq3eUKFiyopk2b6syZMzpy5Ij27Nnj6pEAAAAA3Obc4lA66eKeotjYWLVo0UKlSpXS+PHj1aZNG+3atcvVowEAAAC4zbnFHqMOHTpo8eLFKliwoNq3b69XXnlFtWvXdvVYAAAAAAzhFmHk6empefPmqWnTpvL09HT1OAAAAAAM4xZhFBsb6+oRAAAAABjMZWE0ceJE9e7dWz4+Ppo4ceJ11x00aFA+TQUAAADARC67XHdERIR+/PFHFSlSRBEREddcz2az6eDBgznaNpfrBgBIXK4bAHALXK770KFDV/1vAAAAAMhvbnG57piYGCUnJ2dZfv78ecXExLhgIgAAAAAmcdmhdJfz9PRUQkKCQkJCnJafOnVKISEhysjIyNH2OJQOACBxKB0A4BY4lO5ylmXJZrNlWb59+3YFBwe7YCLA/dStUV5DujRWjaiyKlEsUO2HfKhFa3Y4rfNK30fUrU0dBQX4auP2gxo0eq4OHD3puP2L8c+oWoVSKhYcoDPnkrV60169PPEbJZxMzO+nAwDIJT/9uFmffvKx9uz+WSdPntR7E9/XQ40aS5LS0tI0eeJ4xa1fp99++1UB/v6qVbuOBg8ZqpCQ4i6eHHAvLj2UrnDhwgoODpbNZlOFChUUHBzs+AgMDNTDDz+s9u3bu3JEwG34+dq1c98xPTtm7lVvH9q1sfo91UCDRn+u+l3e0d/nU7Xo/f6ye///3z/Wbd6nzi98omptYtRx+HSVK1NUn73dI7+eAgAgD5w/n6yKFStqxMuvZrntwoUL+mXPbvXu01dzv/hK4yZM1uFDhzR4QF8XTAq4N5fuMRo/frwsy1L37t01atQoBQYGOm7z9vZWeHi4ateu7cIJAfexfMNuLd+w+5q39+/YUGM/WqbFa3ZKknq+MktHVo7Row2r6YtlP0mSJsWudqx/NOGM3pmxQvPG9ZKXl4fS0zPz9gkAAPLEA/Ua6IF6Da56W0BAgKZNn+G0bMRLr6hThyeUcPy4SpQsmR8jArcEl4ZRdHS0pIuX7q5Tp44KFCjgynGAW1Z4qSIqUSxQqzb94lh2LumCNv98WLXuCneE0eUKFyqoDs3v0Q/bDxFFAGCQpKQk2Ww2BRQq5OpRALfiFucYNWjw/3/luHDhglJTU51uL3Sdf7gpKSlKSUlxWmZlZsjm4Zm7QwJuLLToxX8jJ07/5bT8xKm/VLyI87+f1we1Vp8O9eXna9emHYfUdtDUfJsTAOBaKSkpGj/uHTVv8Yj8/f1dPQ7gVtzict3JyckaMGCAQkJC5Ofnp8KFCzt9XM+YMWMUGBjo9JH+R9a/jgO46L1ZK3V/h7F6pM9kZWRkavprT7t6JABAPkhLS9Pw5wbLsiy99O9Rrh4HcDtuEUbDhw/XqlWrNGXKFNntdk2fPl2jRo1SyZIlNWvWrOved8SIEUpMTHT68CpeM58mB9zD73+ekySFBAc4LQ8pEqA/Tp1zWnbq7N+KP3pCqzb9oi4vzlDzelVU666IfJsVAJD/0tLSNHzos0o4flzTpn/C3iLgKtziULpFixZp1qxZevDBB9WtWzfVq1dPkZGRCgsLU2xsrDp16nTN+9rtdtntdqdlHEYH0xw+dkoJJxPVsFZF7dh3TJIU4Oeje6uE66Mv4q55Pw+Pi5fJ9y7gFj8KAAB54FIUHT1yRNNnzFJQ0PWPxgFM5Ra/DZ0+fVrlypWTdPF8otOnT0uSHnjgAfXty+UkAUny8/VW+TLFHJ+HlyqiuyqU0plzyfr19zN6/7PVeqFnM8UfPanDx07p1X6PKOFkohau3i5JurdKmGpWDtP3Ww/o7F/JiihdTK/2e0QHjp7Uph2HXPW0AAD/UPLff+vo0aOOz4/99pt+2bNHgYGBKlqsmIYNGaQ9e3Zr0vvTlJmRoT9PXnx/u8DAQBXw9nbV2IDbcYswKleunA4dOqSyZcvqzjvv1Lx583Tfffdp0aJFCgoKcvV4gFuoERWm5dMHOz5/a1g7SdLshT+o96tz9O6nK1XQ167JLz+loABffb/tgB7t/4FSUtMlSckX0tT6oWp6uc8j8vP11u9/Jmr593s09qNPlJqW7pLnBAD453bt+lk9u3VxfP7OW2MkSY+2bqM+/QdozepVkqT27Vo73W/6jFm6975a+Tco4OZslmVZrh7ivffek6enpwYNGqSVK1eqVatWsixLaWlpGjdunAYPHnzjjVzG9+4BeTQpAOBWcmbzZFePAABwMZ9s7gpyizC60pEjR/TTTz8pMjJSd911V47vTxgBACTCCACQ/TByi0PprhQWFqawsDBXjwEAAADAEG4RRhMnTrzqcpvNJh8fH0VGRqp+/fry9ORqcwAAAAByn1uE0XvvvaeTJ08qOTnZ8YauZ86cUcGCBeXv768TJ06oXLlyWr16tcqUKePiaQEAAADcbtziDV5Hjx6te++9V/v379epU6d06tQp7du3T7Vq1dKECRN09OhRhYaGasiQIa4eFQAAAMBtyC0uvlC+fHnNnz9f1atXd1q+detWtWvXTgcPHtT333+vdu3aKSEh4Ybb4+ILAACJiy8AALJ/8QW32GOUkJCg9PSs76OSnp6u33//XZJUsmRJ/fXXX/k9GgAAAAADuEUYNWzYUM8884y2bt3qWLZ161b17dtXDz30kCRp586dioiIcNWIAAAAAG5jbhFGH3/8sYKDg1WzZk3Z7XbZ7Xbdc889Cg4O1scffyxJ8vf317vvvuviSQEAAADcjtziHKNLfvnlF+3bt0+SVLFiRVWsWPGmtsM5RgAAiXOMAAC36Bu8litXTjabTeXLl5eXl1uNBgAAAOA25haH0iUnJ6tHjx4qWLCgKleurKNHj0qSBg4cqDfffNPF0wEAAAC43blFGI0YMULbt2/XmjVr5OPj41jeuHFjzZ0714WTAQAAADCBWxyvtmDBAs2dO1f333+/bDabY3nlypV14MABF04GAAAAwARuscfo5MmTCgkJybL877//dgolAAAAAMgLbhFG99xzj5YsWeL4/FIMTZ8+XbVr13bVWAAAAAAM4RaH0o0ePVrNmzfX7t27lZ6ergkTJmj37t36/vvvtXbtWlePBwAAAOA25xZ7jB544AFt27ZN6enpqlq1qpYvX66QkBBt3LhRNWvWdPV4AAAAAG5zbvUGr7mFN3gFAEi8wSsA4BZ5g1cPD48bXlzBZrMpPT09nyYCAAAAYCKXhtHXX399zds2btyoiRMnKjMzMx8nAgAAAGAil4ZR69atsyzbu3evXnzxRS1atEidOnVSTEyMCyYDAAAAYBK3uPiCJB0/fly9evVS1apVlZ6erm3btmnmzJkKCwtz9WgAAAAAbnMuD6PExES98MILioyM1K5du/Tdd99p0aJFqlKliqtHAwAAAGAIlx5K99Zbb2ns2LEKDQ3Vf/7zn6seWgcAAAAAec2ll+v28PCQr6+vGjduLE9Pz2uu99VXX+Vou1yuGwAgcbluAMAtcrnuLl263PBy3QAAAACQ11waRp9++qkrHx4AAAAAJLnBxRcAAAAAwNUIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8QgjAAAAAMYjjAAAAAAYjzACAAAAYDzCCAAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwAAAADGI4wAAAAAGI8wAgAAAGA8wggAAACA8W4qjNavX6/OnTurdu3aOnbsmCRp9uzZiouLy9XhAAAAACA/5DiM5s+fr6ZNm8rX11dbt25VSkqKJCkxMVGjR4/O9QEBAAAAIK/lOIxef/11TZ06VR999JEKFCjgWF63bl1t2bIlV4cDAAAAgPyQ4zDau3ev6tevn2V5YGCgzp49mxszAQAAAEC+ynEYhYaGKj4+PsvyuLg4lStXLleGAgAAAID8lOMw6tWrlwYPHqxNmzbJZrPp+PHjio2N1bBhw9S3b9+8mBEAAAAA8pRXTu/w4osvKjMzU40aNVJycrLq168vu92uYcOGaeDAgXkxIwAAAADkKZtlWdbN3DE1NVXx8fFKSkpSVFSU/P39c3u2m+Z79wBXjwAAcANnNk929QgAABfzyeauoBzvMbrE29tbUVFRN3t3AAAAAHAbOQ6jhg0bymazXfP2VatW/aOBAAAAACC/5TiMqlev7vR5Wlqatm3bpp9//lnR0dG5NRcAAAAA5Jsch9F777131eUjR45UUlLSPx4IAAAAAPLbTV984Urx8fG67777dPr06dzY3D+ybPdJV48AAHADjw3+xNUjAABc7PyKF7K1Xo7fx+haNm7cKB8fn9zaHAAAAADkmxwfSte2bVunzy3LUkJCgn788Ue98soruTYYAAAAAOSXHIdRYGCg0+ceHh6qWLGiYmJi1KRJk1wbDAAAAADyS47CKCMjQ926dVPVqlVVuHDhvJoJAAAAAPJVjs4x8vT0VJMmTXT27Nk8GgcAAAAA8l+OL75QpUoVHTx4MC9mAQAAAACXyHEYvf766xo2bJgWL16shIQEnTt3zukDAAAAAG412T7HKCYmRkOHDlWLFi0kSY8++qhsNpvjdsuyZLPZlJGRkftTAgAAAEAeynYYjRo1Sn369NHq1avzch4AAAAAyHfZDiPLsiRJDRo0yLNhAAAAAMAVcnSO0eWHzgEAAADA7SJH72NUoUKFG8bR6dOn/9FAAAAAAJDfchRGo0aNUmBgYF7NAgAAAAAukaMw6tChg0JCQvJqFgAAAABwiWyfY8T5RQAAAABuV9kOo0tXpQMAAACA2022D6XLzMzMyzkAAAAAwGVydLluAAAAALgdEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIznNmG0fv16de7cWbVr19axY8ckSbNnz1ZcXJyLJwMAAABwu3OLMJo/f76aNm0qX19fbd26VSkpKZKkxMREjR492sXTAQAAALjduUUYvf7665o6dao++ugjFShQwLG8bt262rJliwsnAwAAAGACtwijvXv3qn79+lmWBwYG6uzZs/k/EAAAAACjuEUYhYaGKj4+PsvyuLg4lStXzgUTAQAAADCJW4RRr169NHjwYG3atEk2m03Hjx9XbGyshg0bpr59+7p6PAAAAAC3OS9XDyBJL774ojIzM9WoUSMlJyerfv36stvtGjZsmAYOHOjq8QAAAADc5myWZVmuHuKS1NRUxcfHKykpSVFRUfL397+p7SzbfTKXJwMA3IoeG/yJq0cAALjY+RUvZGs9tziUbs6cOUpOTpa3t7eioqJ033333XQUAQAAAEBOuUUYDRkyRCEhIerYsaOWLl2qjIwMV48EAAAAwCBuEUYJCQn6/PPPZbPZ1L59e5UoUUL9+/fX999/7+rRAAAAABjALcLIy8tLLVu2VGxsrE6cOKH33ntPhw8fVsOGDVW+fHlXjwcAAADgNucWV6W7XMGCBdW0aVOdOXNGR44c0Z49e1w9EgAAAIDbnFvsMZKk5ORkxcbGqkWLFipVqpTGjx+vNm3aaNeuXa4eDQAAAMBtzi32GHXo0EGLFy9WwYIF1b59e73yyiuqXbu2q8cCAAAAYAi3CCNPT0/NmzdPTZs2laenp6vHAQAAAGAYtwij2NhYV48AAAAAwGAuC6OJEyeqd+/e8vHx0cSJE6+77qBBg/JpKgAAAAAmslmWZbnigSMiIvTjjz+qSJEiioiIuOZ6NptNBw8ezNG2l+0++U/HA9ze+v9+rQ3/XaBTJxIkSSXKRKhZ+66Kqnnx/LyTCcf0zczJOrBnp9LTUlXp7lp6vNcQFQoKduXYQL56bPAnrh4ByBf+vt56tWs9PVr3DhULKqjt8Sc07IOV+mnf7451KpYtotd7NlC9u8rKy8OmX46e0lOjvtavJ/9y4eRA3ju/4oVsreeyMMpLhBFMsHNznDw8PFWsRGnJsvS/1d/qu2/+o+ff/UTBISX05rPRKhUeqRZP9ZAkLflsuhJP/6nnxk6Th4fbXJASyFOEEUwx+6VHFRVeTIMmLlPCqSQ91aiyBra7VzV6TNfxU0mKKBGk9ZO7aOa3OzRv9W6dS05VVHhR/W/PcZ08m+zq8YE8ld0wcovfjmJiYpScnPUf5fnz5xUTE+OCiQD3V/XeB1S5Zm2FlCyjkFJl1bLzM7L7+Orwvt06+MtOnT75uzoNekklw8qrZFh5dR70kn498Iv27/zJ1aMDAHKRj7eXHqtXUS99tFobdv6mg8fP6o3ZG3Tg2Bn1anW3JGlUt/pa9r8Demn6Gm0/cEKHEs5qycZ4ogi4jFuE0ahRo5SUlJRleXJyskaNGuWCiYBbS2ZGhn5av1IpFy4ovGJlpaelyiabvAoUcKzj5e0tm81DB/bscOGkAIDc5uXpIS9PD11Iy3BafiE1XXWqlJbNJjWrVU77fzujhWPa68i8AVo38Wm1qnOHiyYG3JNbhJFlWbLZbFmWb9++XcHB1z8fIiUlRefOnXP6SE1NyatRAbdy/MgBDXvqYT3X/iHNm/qOer44WiXKRCi8QmV5+/ho4awpSk25oJQL5/XNp+8rMzND586ccvXYAIBclHQ+VT/sOqYRneqoRBF/eXjY1KFRlGpVKqnQYD+FBPkpoKBdw56spRWbD6rViHlauGGfPn+1jR64q4yrxwfchksv1124cGHZbDbZbDZVqFDBKY4yMjKUlJSkPn36XHcbY8aMybJXqVO/YXq6//N5MjPgTkJKltUL42bofHKStn2/RnMmvqFBr09SiTIR6jb8Nc2b+o7WLflSNpuHatRrrNLlKshmc4u/hwAAclH3sYs1bVhzHfy8v9IzMrVt/++at3qP7q4QKg+Pi79fLd4Yr0lf/ShJ2nHghGpVLqVeLasrbsevrhwdcBsuDaPx48fLsix1795do0aNUmBgoOM2b29vhYeHq3bt2tfdxogRI/Tcc885LVt78FyezAu4G68CBS5efEFS2fJ36mj8Hq1d/IU69H1elarfp1enzlPSubPy8PRUQb8AvdTtURUtXtLFUwMActuhhLNqMvQ/KuhTQIUKeuv3039r9kuP6lDCWf2ZmKy09AztOfKn0332Hj2lOlVKu2hiwP24NIyio6MlXbx0d506dVTgsvMhsstut8tutzst8/bmUDqYycq0lJ6W5rTMv1CQJGnfjp+UlHhGVe57wAWTAQDyQ/KFNCVfSFOQv12N74nQSx+tUVp6pn7a+7sqlHE+PeGOUsE6+gd/TAYucVkYnTt3ToUKFZIk3X333Tp//rzOnz9/1XUvrQfg/y2cPVVRNe5X4WLFlXI+WT+uW6H4XVvV99/jJEk/fLdExUuHyb9QYR3e+7PmfzxBD7Zqr+Klyrp4cgBAbmt8T4Rskvb9dlrlSxbW6N4Pat+vpzVr2U5J0ntfbNLsl1orbsdvWrv9iJrcW04takeq6dDPXDs44EZcFkaFCxdWQkKCQkJCFBQUdNWLL1y6KENGRsZVtgCYLSnxjOZMeF2JZ07Jt6CfSoaXV99/j9Od1e+VJJ04dlSL5kxTctI5BRcLVZPHu6jho0+6eGoAQF4ILGhXTI/6KlU0QKf/uqBv4vbq1U/WKT0jU5K0cMN+DZywTMOful/v9m+kfb+d1lOjvtb3u465eHLAfbjsDV7Xrl2runXrysvLS2vXrr3uug0aNMjRtnmDVwCAxBu8AgCy/wavLttjdHns5DR8AAAAACA3ucV1e//73/8qLi7O8fn777+v6tWrq2PHjjpz5owLJwMAAABgArcIo+HDh+vcuYtXRdm5c6eee+45tWjRQocOHcpyKW4AAAAAyG0uvVz3JYcOHVJUVJQkaf78+WrVqpVGjx6tLVu2qEWLFi6eDgAAAMDtzi32GHl7eys5OVmStHLlSjVp0kSSFBwc7NiTBAAAAAB5xS32GD3wwAN67rnnVLduXf3vf//T3LlzJUn79u1T6dK8IzMAAACAvOUWe4wmT54sLy8vffnll5oyZYpKlSolSfr222/VrFkzF08HAAAA4Hbnsvcxyku8jxEAQOJ9jAAAt8D7GF0pIyNDCxYs0J49eyRJlStX1qOPPipPT08XTwYAAADgducWYRQfH68WLVro2LFjqlixoiRpzJgxKlOmjJYsWaLy5cu7eEIAAAAAtzO3OMdo0KBBKl++vH799Vdt2bJFW7Zs0dGjRxUREaFBgwa5ejwAAAAAtzm32GO0du1a/fDDDwoODnYsK1KkiN58803VrVvXhZMBAAAAMIFb7DGy2+3666+/sixPSkqSt7e3CyYCAAAAYBK3CKOWLVuqd+/e2rRpkyzLkmVZ+uGHH9SnTx89+uijrh4PAAAAwG3OLcJo4sSJioyMVJ06deTj4yMfHx/VrVtXkZGRmjBhgqvHAwAAAHCbc+k5RpmZmXr77be1cOFCpaam6rHHHlN0dLRsNpsqVaqkyMhIV44HAAAAwBAuDaM33nhDI0eOVOPGjeXr66ulS5cqMDBQn3zCG/IBAAAAyD8uPZRu1qxZ+uCDD7Rs2TItWLBAixYtUmxsrDIzM105FgAAAADDuDSMjh49qhYtWjg+b9y4sWw2m44fP+7CqQAAAACYxqVhlJ6eLh8fH6dlBQoUUFpamosmAgAAAGAil55jZFmWunbtKrvd7lh24cIF9enTR35+fo5lX331lSvGAwAAAGAIl4ZRdHR0lmWdO3d2wSQAAAAATObSMJoxY4YrHx4AAAAAJLnJG7wCAAAAgCsRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADAeYQQAAADAeIQRAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjGezLMty9RAAcldKSorGjBmjESNGyG63u3ocAICL8P8DIPsII+A2dO7cOQUGBioxMVGFChVy9TgAABfh/wdA9nEoHQAAAADjEUYAAAAAjEcYAQAAADAeYQTchux2u1599VVOtAUAw/H/AyD7uPgCAAAAAOOxxwgAAACA8QgjAAAAAMYjjAAAAAAYjzACoPDwcI0fP97VYwAAcsGaNWtks9l09uzZ667Hz37AGWEE5LGuXbvKZrPpzTffdFq+YMEC2Wy2fJ3l008/VVBQUJblmzdvVu/evfN1FgAw3aX/P9hsNnl7eysyMlIxMTFKT0//R9utU6eOEhISFBgYKImf/UB2EUZAPvDx8dHYsWN15swZV49yVcWKFVPBggVdPQYAGKdZs2ZKSEjQ/v37NXToUI0cOVJvv/32P9qmt7e3QkNDb/jHN372A84IIyAfNG7cWKGhoRozZsw114mLi1O9evXk6+urMmXKaNCgQfr7778dtyckJOiRRx6Rr6+vIiIi9Nlnn2U5DGLcuHGqWrWq/Pz8VKZMGfXr109JSUmSLh5a0a1bNyUmJjr+Qjly5EhJzodTdOzYUU8++aTTbGlpaSpatKhmzZolScrMzNSYMWMUEREhX19fVatWTV9++WUuvFIAYBa73a7Q0FCFhYWpb9++aty4sRYuXKgzZ86oS5cuKly4sAoWLKjmzZtr//79jvsdOXJErVq1UuHCheXn56fKlStr6dKlkpwPpeNnP5B9hBGQDzw9PTV69GhNmjRJv/32W5bbDxw4oGbNmqldu3basWOH5s6dq7i4OA0YMMCxTpcuXXT8+HGtWbNG8+fP14cffqgTJ044bcfDw0MTJ07Url27NHPmTK1atUrPP/+8pIuHVowfP16FChVSQkKCEhISNGzYsCyzdOrUSYsWLXIElSQtW7ZMycnJatOmjSRpzJgxmjVrlqZOnapdu3ZpyJAh6ty5s9auXZsrrxcAmMrX11epqanq2rWrfvzxRy1cuFAbN26UZVlq0aKF0tLSJEn9+/dXSkqK1q1bp507d2rs2LHy9/fPsj1+9gM5YAHIU9HR0Vbr1q0ty7Ks+++/3+revbtlWZb19ddfW5f+Cfbo0cPq3bu30/3Wr19veXh4WOfPn7f27NljSbI2b97suH3//v2WJOu999675mN/8cUXVpEiRRyfz5gxwwoMDMyyXlhYmGM7aWlpVtGiRa1Zs2Y5bn/qqaesJ5980rIsy7pw4YJVsGBB6/vvv3faRo8ePaynnnrq+i8GAMDh8v8/ZGZmWitWrLDsdrv12GOPWZKsDRs2ONb9888/LV9fX2vevHmWZVlW1apVrZEjR151u6tXr7YkWWfOnLEsi5/9QHZ5ubTKAMOMHTtWDz30UJa/1m3fvl07duxQbGysY5llWcrMzNShQ4e0b98+eXl5qUaNGo7bIyMjVbhwYaftrFy5UmPGjNEvv/yic+fOKT09XRcuXFBycnK2jyP38vJS+/btFRsbq6efflp///23vvnmG33++eeSpPj4eCUnJ+vhhx92ul9qaqruvvvuHL0eAGC6xYsXy9/fX2lpacrMzFTHjh3Vtm1bLV68WLVq1XKsV6RIEVWsWFF79uyRJA0aNEh9+/bV8uXL1bhxY7Vr10533XXXTc/Bz35AIoyAfFS/fn01bdpUI0aMUNeuXR3Lk5KS9Mwzz2jQoEFZ7lO2bFnt27fvhts+fPiwWrZsqb59++qNN95QcHCw4uLi1KNHD6WmpuboBNtOnTqpQYMGOnHihFasWCFfX181a9bMMaskLVmyRKVKlXK6n91uz/ZjAACkhg0basqUKfL29lbJkiXl5eWlhQsX3vB+PXv2VNOmTbVkyRItX75cY8aM0bvvvquBAwfe9Cz87IfpCCMgn7355puqXr26Klas6FhWo0YN7d69W5GRkVe9T8WKFZWenq6tW7eqZs2aki7+9e7yq9z99NNPyszM1LvvvisPj4unD86bN89pO97e3srIyLjhjHXq1FGZMmU0d+5cffvtt3riiSdUoEABSVJUVJTsdruOHj2qBg0a5OzJAwCc+Pn5ZfnZX6lSJaWnp2vTpk2qU6eOJOnUqVPau3evoqKiHOuVKVNGffr0UZ8+fTRixAh99NFHVw0jfvYD2UMYAfmsatWq6tSpkyZOnOhY9sILL+j+++/XgAED1LNnT/n5+Wn37t1asWKFJk+erDvvvFONGzdW7969NWXKFBUoUEBDhw6Vr6+v43KskZGRSktL06RJk9SqVStt2LBBU6dOdXrs8PBwJSUl6bvvvlO1atVUsGDBa+5J6tixo6ZOnap9+/Zp9erVjuUBAQEaNmyYhgwZoszMTD3wwANKTEzUhg0bVKhQIUVHR+fBqwYA5rjjjjvUunVr9erVS9OmTVNAQIBefPFFlSpVSq1bt5YkPfvss2revLkqVKigM2fOaPXq1apUqdJVt8fPfiB7uCod4AIxMTHKzMx0fH7XXXdp7dq12rdvn+rVq6e7775b//73v1WyZEnHOrNmzVLx4sVVv359tWnTRr169VJAQIB8fHwkSdWqVdO4ceM0duxYValSRbGxsVkuD16nTh316dNHTz75pIoVK6a33nrrmjN26tRJu3fvVqlSpVS3bl2n21577TW98sorGjNmjCpVqqRmzZppyZIlioiIyI2XBwCMN2PGDNWsWVMtW7ZU7dq1ZVmWli5d6tiDk5GRof79+zt+BleoUEEffPDBVbfFz34ge2yWZVmuHgJAzv32228qU6aMVq5cqUaNGrl6HAAAgFsaYQTcIlatWqWkpCRVrVpVCQkJev7553Xs2DHt27fP8RdEAAAA3BzOMQJuEWlpafrXv/6lgwcPKiAgQHXq1FFsbCxRBAAAkAvYYwQAAADAeFx8AQAAAIDxCCMAAAAAxiOMAAAAABiPMAIAAABgPMIIAAAAgPEIIwDALa9r16567LHHHJ8/+OCDevbZZ/N9jjVr1shms+ns2bP5/tgAgH+GMAIA5JmuXbvKZrPJZrPJ29tbkZGRiomJUXp6ep4+7ldffaXXXnstW+sSMwAAiTd4BQDksWbNmmnGjBlKSUnR0qVL1b9/fxUoUEAjRoxwWi81NVXe3t658pjBwcG5sh0AgDnYYwQAyFN2u12hoaEKCwtT37591bhxYy1cuNBx+Nsbb7yhkiVLqmLFipKkX3/9Ve3bt1dQUJCCg4PVunVrHT582LG9jIwMPffccwoKClKRIkX0/PPP68r3Kr/yULqUlBS98MILKlOmjOx2uyIjI/Xxxx/r8OHDatiwoSSpcOHCstls6tq1qyQpMzNTY8aMUUREhHx9fVWtWjV9+eWXTo+zdOlSVahQQb6+vmrYsKHTnACAWwthBADIV76+vkpNTZUkfffdd9q7d69WrFihxYsXKy0tTU2bNlVAQIDWr1+vDRs2yN/fX82aNXPc591339Wnn36qTz75RHFxcTp9+rS+/vrr6z5mly5d9J///EcTJ07Unj17NG3aNPn7+6tMmTKaP3++JGnv3r1KSEjQhAkTJEljxozRrFmzNHXqVO3atUtDhgxR586dtXbtWkkXA65t27Zq1aqVtm3bpp49e+rFF1/Mq5cNAJDHOJQOAJAvLMvSd999p2XLlmngwIE6efKk/Pz8NH36dMchdHPmzFFmZqamT58um80mSZoxY4aCgoK0Zs0aNWnSROPHj9eIESPUtm1bSdLUqVO1bNmyaz7uvn37NG/ePK1YsUKNGzeWJJUrV85x+6XD7kJCQhQUFCTp4h6m0aNHa+XKlapdu7bjPnFxcZo2bZoaNGigKVOmqHz58nr33XclSRUrVtTOnTs1duzYXHzVAAD5hTACAOSpxYsXy9/fX2lpacrMzFTHjh01cuRI9e/fX1WrVnU6r2j79u2Kj49XQECA0zYuXLigAwcOKDExUQkJCapVq5bjNi8vL91zzz1ZDqe7ZNu2bfL09FSDBg2yPXN8fLySk5P18MMPOy1PTU3V3XffLUnas2eP0xySHBEFALj1EEYAgDzVsGFDTZkyRd7e3ipZsqS8vP7/fz1+fn5O6yYlJalmzZqKjY3Nsp1ixYrd1OP7+vrm+D5JSUmSpCVLlqhUqVJOt9nt9puaAwDg3ggjAECe8vPzU2RkZLbWrVGjhubOnauQkBAVKlToquuUKFFCmzZtUv369SVJ6enp+umnn1SjRo2rrl+1alVlZmZq7dq1jkPpLndpj1VGRoZjWVRUlOx2u44ePXrNPU2VKlXSwoULnZb98MMPN36SAAC3xMUXAABuo1OnTipatKhat26t9evX69ChQ1qzZo0GDRqk3377TZI0ePBgvfnmm1qwYIF++eUX9evX77rvQRQeHq7o6Gh1795dCxYscGxz3rx5kqSwsDDZbDYtXrxYJ0+eVFJSkgICAjRs2DANGTJEM2fO1IEDB7RlyxZNmjRJM2fOlCT16dNH+/fv1/Dhw7V371599tln+vTTT/P6JQIA5BHCCADgNgoWLKh169apbNmyatu2rSpVqqQePXrowoULjj1IQ4cO1dNPP63o6GjVrl1bAQEBatOmzXW3O2XKFD3++OPq16+f7rzzTvXq1Ut///23JKlUqVIaNWqUXnzxRRUvXlwDBgyQJL322mt65ZVXNGbMGFWqVEnNmjXTkiVLFBERIUkqW7as5s+frwULFqhatWqaOnWqRo8enYevDgAgL9msa52tCgAAAACGYI8RAAAAAOMRRgAAAACMRxgBAAAAMB5hBAAAAMB4hBEAAAAA4xFGAAAAAIxHGAEAAAAwHmEEAAAAwHiEEQAAAADjEUYAAAAAjEcYAQAAADDe/wFujjV2IDHHcQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for GaussianNB:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.73 0.90 0.80 115\n", + " 1 0.89 0.71 0.79 135\n", + "\n", + " accuracy 0.80 250\n", + " macro avg 0.81 0.80 0.80 250\n", + "weighted avg 0.81 0.80 0.80 250\n", + "\u001b[0m\n", + "\u001b[32mModel GaussianNB saved as models/GaussianNB_2024-08-01.pkl\u001b[0m\n", + "\u001b[33mTraining on Model: BernoulliNB\u001b[0m\n", + "\u001b[32mTraining on Model: BernoulliNB complete.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36mClassification Report for BernoulliNB:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.46 0.97 0.62 115\n", + " 1 0.40 0.01 0.03 135\n", + "\n", + " accuracy 0.46 250\n", + " macro avg 0.43 0.49 0.33 250\n", + "weighted avg 0.43 0.46 0.30 250\n", + "\u001b[0m\n", + "\u001b[32mModel BernoulliNB saved as models/BernoulliNB_2024-08-01.pkl\u001b[0m\n", + "\u001b[32mTraining on multiple models complete. Returning results of training as a dataframe.\u001b[0m\n", + "\u001b[36m Classifier Accuracy F1-Score \\\n", + "0 KNeighborsClassifier 0.772 0.771923 \n", + "1 LogisticRegression 0.848 0.848156 \n", + "2 DecisionTreeClassifier 0.856 0.856222 \n", + "3 RandomForestClassifier 0.888 0.888172 \n", + "4 GradientBoostingClassifier 0.880 0.880192 \n", + "5 SVC 0.864 0.864209 \n", + "6 GaussianNB 0.796 0.795383 \n", + "7 BernoulliNB 0.456 0.301651 \n", + "\n", + " Best Parameters \n", + "0 {'n_neighbors': 7, 'weights': 'uniform'} \n", + "1 {'C': 10, 'solver': 'liblinear'} \n", + "2 {'criterion': 'entropy', 'max_depth': 20} \n", + "3 {'max_depth': None, 'n_estimators': 200} \n", + "4 {'learning_rate': 0.01, 'n_estimators': 100} \n", + "5 {'C': 10, 'kernel': 'linear'} \n", + "6 {} \n", + "7 {} \u001b[0m\n" + ] + } + ], "source": [ "# Generate synthetic dataset for testing\n", "X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)\n", @@ -66,9 +329,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ClassifierAccuracyF1-ScoreBest Parameters
0KNeighborsClassifier0.7720.771923{'n_neighbors': 7, 'weights': 'uniform'}
1LogisticRegression0.8480.848156{'C': 10, 'solver': 'liblinear'}
2DecisionTreeClassifier0.8560.856222{'criterion': 'entropy', 'max_depth': 20}
3RandomForestClassifier0.8880.888172{'max_depth': None, 'n_estimators': 200}
4GradientBoostingClassifier0.8800.880192{'learning_rate': 0.01, 'n_estimators': 100}
5SVC0.8640.864209{'C': 10, 'kernel': 'linear'}
6GaussianNB0.7960.795383{}
7BernoulliNB0.4560.301651{}
\n", + "
" + ], + "text/plain": [ + " Classifier Accuracy F1-Score \\\n", + "0 KNeighborsClassifier 0.772 0.771923 \n", + "1 LogisticRegression 0.848 0.848156 \n", + "2 DecisionTreeClassifier 0.856 0.856222 \n", + "3 RandomForestClassifier 0.888 0.888172 \n", + "4 GradientBoostingClassifier 0.880 0.880192 \n", + "5 SVC 0.864 0.864209 \n", + "6 GaussianNB 0.796 0.795383 \n", + "7 BernoulliNB 0.456 0.301651 \n", + "\n", + " Best Parameters \n", + "0 {'n_neighbors': 7, 'weights': 'uniform'} \n", + "1 {'C': 10, 'solver': 'liblinear'} \n", + "2 {'criterion': 'entropy', 'max_depth': 20} \n", + "3 {'max_depth': None, 'n_estimators': 200} \n", + "4 {'learning_rate': 0.01, 'n_estimators': 100} \n", + "5 {'C': 10, 'kernel': 'linear'} \n", + "6 {} \n", + "7 {} " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results.head(10)" ] @@ -83,9 +461,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cleanup completed.\n" + ] + } + ], "source": [ "root = \"./\"\n", "for path, subdirs, files in os.walk(root):\n",