notebooks/20200308 - First experiments.ipynb

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   "source": [
    "# Base Data Science snippet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating data"
   ]
  },
  {
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   "source": [
    "Case 1 - OR problem\n",
    "\n",
    "- Rain -> Wet\n",
    "- Rain -> not Sprinkler\n",
    "- Sprinkler -> Wet\n",
    "\n",
    "Case 2 - XOR problem\n",
    "Two light buttons to switch on"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rain</th>\n",
       "      <th>sprinkler</th>\n",
       "      <th>wet</th>\n",
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      "text/plain": [
       "   rain  sprinkler  wet\n",
       "0     0          0    0\n",
       "1     0          1    1\n",
       "2     1          0    1"
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   "source": [
    "data = [\n",
    "    [0,0,0],\n",
    "    [0,1,1],\n",
    "    [1,0,1],\n",
    "]\n",
    "\n",
    "data = pd.DataFrame(data,columns = [\"rain\",\"sprinkler\",\"wet\"])\n",
    "X = data[[\"rain\",\"sprinkler\"]]\n",
    "y = data[\"wet\"]\n",
    "data"
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   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "lr = LogisticRegression()\n",
    "rf = RandomForestClassifier()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
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       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
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   "source": [
    "lr.fit(X,y)\n",
    "rf.fit(X,y)"
   ]
  },
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     "text": [
      "-- Classes\n",
      "[1 1 1]\n",
      "[0 1 1]\n",
      "\n",
      "-- Probabilities\n",
      "[[0.45383769 0.54616231]\n",
      " [0.36566869 0.63433131]\n",
      " [0.36566869 0.63433131]]\n",
      "[[0.7 0.3]\n",
      " [0.2 0.8]\n",
      " [0.4 0.6]]\n"
     ]
    }
   ],
   "source": [
    "print(\"-- Classes\")\n",
    "print(lr.predict(X))\n",
    "print(rf.predict(X))\n",
    "\n",
    "print(\"\\n-- Probabilities\")\n",
    "print(lr.predict_proba(X))\n",
    "print(rf.predict_proba(X))"
   ]
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     "text": [
      "-- Classes\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "print(\"-- Classes\")\n",
    "print(rf.predict([[1,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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