07_regression.ipynb

      "  SOCKEYE  ONLY :  best_score    = -2.95\n",
      "BOTH COMBINED   :  average_score = -4.19\n",
      "\n",
      "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
      "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "           min_impurity_split=None, min_samples_leaf=1,\n",
      "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "           presort=False, random_state=None, splitter='best')\n",
      "ONE MODEL       :  best_score    = -4.60\n",
      "  ATLANTIC ONLY :  best_score    = -5.52\n",
      "  SOCKEYE  ONLY :  best_score    = -2.98\n",
      "BOTH COMBINED   :  average_score = -4.25\n",
      "\n",
      "KernelRidge(alpha=1, coef0=1, degree=3, gamma=None, kernel='linear',\n",
      "      kernel_params=None)\n",
      "ONE MODEL       :  best_score    = -4.52\n",
      "  ATLANTIC ONLY :  best_score    = -5.63\n",
      "  SOCKEYE  ONLY :  best_score    = -3.08\n",
      "BOTH COMBINED   :  average_score = -4.35\n",
      "\n",
      "KernelRidge(alpha=1, coef0=1, degree=3, gamma=None, kernel='rbf',\n",
      "      kernel_params=None)\n",
      "ONE MODEL       :  best_score    = -4.57\n",
      "  ATLANTIC ONLY :  best_score    = -5.89\n",
      "  SOCKEYE  ONLY :  best_score    = -3.18\n",
      "BOTH COMBINED   :  average_score = -4.54\n",
      "\n",
      "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
      "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "ONE MODEL       :  best_score    = -4.38\n",
      "  ATLANTIC ONLY :  best_score    = -5.56\n",
      "  SOCKEYE  ONLY :  best_score    = -3.13\n",
      "BOTH COMBINED   :  average_score = -4.35\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "sockey_indices = features[\"kind\"] == 1\n",
    "features_sockeye = features[sockey_indices]\n",
    "values_sockeye = values[sockey_indices]\n",
    "\n",
    "atlantic_indices = features[\"kind\"] == 0\n",
    "features_atlantic = features[atlantic_indices]\n",
    "values_atlantic = values[atlantic_indices]\n",
    "\n",
    "\n",
    "def eval_clf(clf):\n",
    "    print(clf)\n",
    "    p = make_pipeline(PolynomialFeatures(), PCA(), DecisionTreeRegressor())\n",
    "\n",
    "    param_grid = {'polynomialfeatures__degree': range(3, 12),\n",
    "                  'pca__n_components': range(1, 10),\n",
    "                 }\n",
    "\n",
    "    search = GridSearchCV(p, param_grid, scoring=\"neg_mean_absolute_error\", cv=4, n_jobs=4)\n",
    "\n",
    "    search.fit(features, values)\n",
    "    print(\"ONE MODEL       :  best_score    = {:.2f}\".format(search.best_score_))\n",
    "    score_full = search.best_score_\n",
    "\n",
    "    search.fit(features_atlantic, values_atlantic)\n",
    "    print(\"  ATLANTIC ONLY :  best_score    = {:.2f}\".format(search.best_score_))\n",
    "    score_atlantic = search.best_score_\n",
    "\n",
    "    search.fit(features_sockeye, values_sockeye)\n",
    "    print(\"  SOCKEYE  ONLY :  best_score    = {:.2f}\".format(search.best_score_))\n",
    "    score_sockeye = search.best_score_\n",
    "\n",
    "    print(\"BOTH COMBINED   :  average_score = {:.2f}\".format((score_atlantic + score_sockeye) / 2.0))\n",
    "    print()\n",
    "    \n",
    "    \n",
    "eval_clf(LinearRegression())\n",
    "eval_clf(DecisionTreeRegressor())\n",
    "eval_clf(KernelRidge())\n",
    "eval_clf(KernelRidge(kernel=\"rbf\"))\n",
    "eval_clf(SVR())"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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