[pre-commit.ci] auto fixes from pre-commit hooks

Author: pre-commit-ci[bot]

docker/vitis_cfg.txt

@@ -30,4 +30,3 @@ CreateFileAssociation=1
 
 # Choose whether disk usage will be optimized (reduced) after installation
 EnableDiskUsageOptimization=1
-

part5_bdt.ipynb

@@ -40,6 +40,7 @@
     "\n",
     "# enable more output from conifer\n",
     "import logging\n",
+    "\n",
     "logging.basicConfig(stream=sys.stdout, level=logging.WARNING)\n",
     "logger = logging.getLogger('conifer')\n",
     "logger.setLevel('DEBUG')\n",
@@ -112,8 +113,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "clf = xgb.XGBClassifier(n_estimators=25, max_depth=5, learning_rate=1.0,\n",
-    "                        random_state=seed).fit(X_train_val, y_train_val)"
+    "clf = xgb.XGBClassifier(n_estimators=25, max_depth=5, learning_rate=1.0, random_state=seed).fit(X_train_val, y_train_val)"
    ]
   },
   {
@@ -150,7 +150,10 @@
     "# add a legend\n",
     "from matplotlib.lines import Line2D\n",
     "\n",
-    "lines = [Line2D([0], [0], ls='--'), Line2D([0], [0], ls='-'),]\n",
+    "lines = [\n",
+    "    Line2D([0], [0], ls='--'),\n",
+    "    Line2D([0], [0], ls='-'),\n",
+    "]\n",
     "from matplotlib.legend import Legend\n",
     "\n",
     "leg = Legend(ax, lines, labels=['part1 Keras', 'xgboost'], loc='lower right', frameon=False)\n",
@@ -222,7 +225,7 @@
     "# print the help to see the API on the conifer_model\n",
     "help(conifer_model)\n",
     "# write the project (writing HLS project to disk)\n",
-    "conifer_model.write() \n",
+    "conifer_model.write()\n",
     "# save the conifer model - we can load this again later\n",
     "clf.save_model('model_5/xgboost_model.json')"
    ]
@@ -301,7 +304,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_hls_proba = softmax(y_hls) # compute class probabilities from the raw predictions\n",
+    "y_hls_proba = softmax(y_hls)  # compute class probabilities from the raw predictions\n",
     "\n",
     "print(f'Accuracy baseline:  {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_ref, axis=1)):.5f}')\n",
     "print(f'Accuracy xgboost:   {accuracy_score(np.argmax(y_test_one_hot, axis=1), np.argmax(y_xgb, axis=1)):.5f}')\n",
@@ -318,7 +321,11 @@
     "# add a legend\n",
     "from matplotlib.lines import Line2D\n",
     "\n",
-    "lines = [Line2D([0], [0], ls='--'), Line2D([0], [0], ls=':'), Line2D([0], [0], ls='-'),]\n",
+    "lines = [\n",
+    "    Line2D([0], [0], ls='--'),\n",
+    "    Line2D([0], [0], ls=':'),\n",
+    "    Line2D([0], [0], ls='-'),\n",
+    "]\n",
     "from matplotlib.legend import Legend\n",
     "\n",
     "leg = Legend(ax, lines, labels=['part1 Keras', 'xgboost', 'conifer'], loc='lower right', frameon=False)\n",
@@ -390,11 +397,12 @@
    "outputs": [],
    "source": [
     "pynq_model_cfg = conifer.backends.xilinxhls.auto_config()\n",
-    "pynq_model_cfg['OutputDir'] = 'model_5_pynq'          # choose a new project directory\n",
+    "pynq_model_cfg['OutputDir'] = 'model_5_pynq'  # choose a new project directory\n",
     "pynq_model_cfg['ProjectName'] = 'conifer_jettag'\n",
-    "pynq_model_cfg['AcceleratorConfig'] = {'Board' : 'pynq-z2',      # choose a pynq-z2 board\n",
-    "                                       'InterfaceType' : 'float' # floating point for the data I/O (this is default)\n",
-    "                                      }\n",
+    "pynq_model_cfg['AcceleratorConfig'] = {\n",
+    "    'Board': 'pynq-z2',  # choose a pynq-z2 board\n",
+    "    'InterfaceType': 'float',  # floating point for the data I/O (this is default)\n",
+    "}\n",
     "\n",
     "# print the config\n",
     "print('Modified Configuration\\n' + '-' * 50)\n",

part6_cnns.ipynb

@@ -1546,11 +1546,15 @@
     "import plotting\n",
     "\n",
     "# First, the baseline model\n",
-    "hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name', backend='Vitis', default_precision='ap_fixed<16,6>' )\n",
+    "hls_config = hls4ml.utils.config_from_keras_model(\n",
+    "    model, granularity='name', backend='Vitis', default_precision='ap_fixed<16,6>'\n",
+    ")\n",
     "\n",
     "plotting.print_dict(hls_config)\n",
     "\n",
-    "hls_model = hls4ml.converters.keras_to_hls(model, hls_config=hls_config, output_dir='pruned_cnn', backend='Vitis', part='xcu250-figd2104-2L-e', io_type='io_stream')\n",
+    "hls_model = hls4ml.converters.keras_to_hls(\n",
+    "    model, hls_config=hls_config, output_dir='pruned_cnn', backend='Vitis', part='xcu250-figd2104-2L-e', io_type='io_stream'\n",
+    ")\n",
     "hls_model.compile()"
    ]
   },