remove figure saving

07_linear_models/01_linear_regression_intro.ipynb

@@ -513,7 +513,7 @@
     "plt.axis('off')\n",
     "plt.tight_layout()\n",
     "plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)\n",
-    "# plt.savefig('figures/multiple_regression_summary.png', bbox_inches='tight', dpi=300);"
+    "# plt.savefig('multiple_regression_summary.png', bbox_inches='tight', dpi=300);"
    ]
   },
   {

08_ml4t_workflow/03_backtesting_with_backtrader.ipynb

@@ -397,7 +397,7 @@
    "source": [
     "# cerebro.plot()  # plot the results\n",
     "# figure = cerebro.plot(style='candlebars')[0][0]\n",
-    "# figure.savefig(f'figures/backtrader.png')"
+    "# figure.savefig(f'backtrader.png')"
    ]
   },
   {

12_gradient_boosting_machines/01_boosting_baseline.ipynb

@@ -1764,8 +1764,7 @@
     "axes[1][1].set_ylabel('6-Months Return')\n",
     "fig.suptitle('Partial Dependence Plots', fontsize=16)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=.95)\n",
-    "fig.savefig('figures/partial_dep_2d', dpi=300);"
+    "fig.subplots_adjust(top=.95)\n"
    ]
   },
   {
@@ -1822,8 +1821,7 @@
     "\n",
     "# fig.colorbar(surface)\n",
     "fig.suptitle('Partial Dependence by 6- and 12-month Returns', fontsize=16)\n",
-    "fig.tight_layout()\n",
-    "fig.savefig('figures/partial_dep_3d', dpi=300)"
+    "fig.tight_layout()\n"
    ]
   },
   {

12_gradient_boosting_machines/03_sklearn_gbm_tuning_results.ipynb

@@ -996,7 +996,7 @@
     "fig.suptitle('Mean Test Score Distribution by Hyperparameter', fontsize=14)\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(top=.94)\n",
-    "fig.savefig('figures/sklearn_cv_scores_by_param', dpi=300);"
+    "fig.savefig('sklearn_cv_scores_by_param', dpi=300);"
    ]
   },
   {

12_gradient_boosting_machines/06_evaluate_trading_signals.ipynb

@@ -900,8 +900,7 @@
     "    axes[i].set_title(f'Lookahead: {t} Day(s)')\n",
     "fig.suptitle('OLS Coefficients & Confidence Intervals', fontsize=20)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=.92)\n",
-    "fig.savefig('figures/ols_coefs', dpi=300);"
+    "fig.subplots_adjust(top=.92)\n"
    ]
   },
   {
@@ -2359,8 +2358,7 @@
     "\n",
     "fig.suptitle('3-Month Rolling Information Coefficient', fontsize=16)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=0.92)\n",
-    "fig.savefig('figures/rolling_ic_lgb', dpi=300);"
+    "fig.subplots_adjust(top=0.92)\n";
    ]
   },
   {
@@ -2815,8 +2813,7 @@
     "\n",
     "fig.suptitle('Alphalens - Validation Set Performance', fontsize=14)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=.85)\n",
-    "fig.savefig('figures/alphalens_cv', dpi=300)"
+    "fig.subplots_adjust(top=.85)\n";
    ]
   },
   {
@@ -3943,8 +3940,7 @@
     "\n",
     "fig.suptitle('3-Month Rolling Information Coefficient', fontsize=16)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=0.92)\n",
-    "fig.savefig('figures/rolling_ic_cb', dpi=300);"
+    "fig.subplots_adjust(top=0.92);"
    ]
   },
   {

12_gradient_boosting_machines/07_model_interpretation.ipynb

@@ -570,8 +570,7 @@
     "      rot=0))\n",
     "plt.suptitle('Normalized Importance (Top 20 Features)', fontsize=14)\n",
     "plt.tight_layout()\n",
-    "plt.subplots_adjust(top=.9)\n",
-    "plt.savefig('figures/lgb_fi', dpi=300)"
+    "plt.subplots_adjust(top=.9);"
    ]
   },
   {
@@ -755,8 +754,7 @@
     "axes[1][1].set_ylabel('6-Months Return')\n",
     "fig.suptitle('Partial Dependence Plots', fontsize=16)\n",
     "fig.tight_layout()\n",
-    "fig.subplots_adjust(top=.95)\n",
-    "fig.savefig('figures/partial_dep_2d', dpi=300);"
+    "fig.subplots_adjust(top=.95);"
    ]
   },
   {
@@ -821,8 +819,7 @@
     "\n",
     "# fig.colorbar(surface)\n",
     "fig.suptitle('Partial Dependence by 6- and 12-month Returns', fontsize=16)\n",
-    "fig.tight_layout()\n",
-    "fig.savefig('figures/partial_dep_3d', dpi=300)"
+    "fig.tight_layout();"
    ]
   },
   {
@@ -952,8 +949,7 @@
     "shap_values = explainer.shap_values(X=X)\n",
     "\n",
     "shap.summary_plot(shap_values, X, show=False)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('figures/shap_dots', dpi=300)"
+    "plt.tight_layout();"
    ]
   },
   {
@@ -979,8 +975,7 @@
    ],
    "source": [
     "shap.summary_plot(shap_values, X, plot_type=\"bar\",show=False)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('figures/shap_bar', dpi=300)"
+    "plt.tight_layout();"
    ]
   },
   {

16_word_embeddings/05_financial_news_word2vec_gensim.ipynb

@@ -1257,8 +1257,7 @@
     "(pd.DataFrame(most_sim, columns=['token', 'similarity'])\n",
     " .set_index('token').similarity\n",
     " .sort_values().tail(10).plot.barh(xlim=(.3, .37), ax=axes[1], title='Closest matches for Woman + King - Man'))\n",
-    "fig.tight_layout()\n",
-    "fig.savefig('figures/w2v_evaluation', dpi=300);"
+    "fig.tight_layout();"
    ]
   },
   {

16_word_embeddings/07_sec_word2vec.ipynb

@@ -1307,8 +1307,7 @@
     "(pd.DataFrame(most_sim, columns=['token', 'similarity'])\n",
     " .set_index('token').similarity\n",
     " .sort_values().tail(10).plot.barh(xlim=(.3, .37), ax=axes[1], title='Closest matches for Woman + King - Man'))\n",
-    "fig.tight_layout()\n",
-    "# fig.savefig('figures/w2v_sec_evaluation', dpi=300);"
+    "fig.tight_layout();"
    ]
   }
  ],

24_alpha_factor_library/05_alphalens_analysis.ipynb

@@ -480,8 +480,7 @@
     "                                             freq=pd.tseries.offsets.BDay(),\n",
     "                                             period='1D',\n",
     "                                             ax=axes[2])\n",
-    "fig.tight_layout()\n",
-    "fig.savefig('figures/alphalens', dpi=300)"
+    "fig.tight_layout();"
    ]
   },
   {