diff --git a/docs/evaluation/KShapeAD.csv b/docs/evaluation/KShapeAD.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e3c77fdd0a2793bac88262dcdea207f377bb7890
--- /dev/null
+++ b/docs/evaluation/KShapeAD.csv
@@ -0,0 +1,2 @@
+params,file_name,duration,group,point anomaly,seq anomaly,AUC-PR,AUC-ROC,VUS-PR,VUS-ROC,Standard-F1,PA-F1,Event-based-F1,R-based-F1,Affiliation-F,Recall,Precision
+{'periodicity': 2},280_NEK_id_4_WebService_tr_500_1st_231.csv,0.9505062103271484,NEK,False,False,0.07926785813478894,0.6630577427821522,0.10358866434207449,0.5544817330166786,0.18676697609674528,0.2742857142857143,0.1660457690260775,0.33959170607433914,0.7497700296391373,0.0,0.0
diff --git a/src/datensatz/dataset_investigation.ipynb b/src/datensatz/dataset_investigation.ipynb
index aca8dd10d1d36ec88fb0bb5ed6a52c66bc306bf8..47fb1c071a7729c9b11db9b82f89e0ec112d50f4 100644
--- a/src/datensatz/dataset_investigation.ipynb
+++ b/src/datensatz/dataset_investigation.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -95,7 +95,7 @@
"4 1.219080 0"
]
},
- "execution_count": 13,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -109,7 +109,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -118,7 +118,7 @@
"100000"
]
},
- "execution_count": 14,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -129,7 +129,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -141,7 +141,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 15,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -152,7 +152,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -169,7 +169,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -180,7 +180,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -189,7 +189,7 @@
"(20414183, 3)"
]
},
- "execution_count": 18,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -222,7 +222,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -295,7 +295,7 @@
"4 45.238 0.0 001_NAB_id_1_Facility_tr_1007_1st_2014"
]
},
- "execution_count": 19,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -306,7 +306,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -318,7 +318,7 @@
"Name: count, dtype: int64"
]
},
- "execution_count": 20,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -329,7 +329,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -347,7 +347,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -365,7 +365,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -397,55 +397,55 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>0.779487</td>\n",
+ " <td>0.166970</td>\n",
" <td>0.0</td>\n",
- " <td>233_MGAB_id_9_Synthetic_tr_25000_1st_41300</td>\n",
+ " <td>280_NEK_id_4_WebService_tr_500_1st_231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>0.791644</td>\n",
+ " <td>0.085052</td>\n",
" <td>0.0</td>\n",
- " <td>233_MGAB_id_9_Synthetic_tr_25000_1st_41300</td>\n",
+ " <td>280_NEK_id_4_WebService_tr_500_1st_231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>0.817596</td>\n",
+ " <td>0.080585</td>\n",
" <td>0.0</td>\n",
- " <td>233_MGAB_id_9_Synthetic_tr_25000_1st_41300</td>\n",
+ " <td>280_NEK_id_4_WebService_tr_500_1st_231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>0.831398</td>\n",
+ " <td>0.100995</td>\n",
" <td>0.0</td>\n",
- " <td>233_MGAB_id_9_Synthetic_tr_25000_1st_41300</td>\n",
+ " <td>280_NEK_id_4_WebService_tr_500_1st_231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>0.846866</td>\n",
+ " <td>0.092652</td>\n",
" <td>0.0</td>\n",
- " <td>233_MGAB_id_9_Synthetic_tr_25000_1st_41300</td>\n",
+ " <td>280_NEK_id_4_WebService_tr_500_1st_231</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " Data Label FileName\n",
- "0 0.779487 0.0 233_MGAB_id_9_Synthetic_tr_25000_1st_41300\n",
- "1 0.791644 0.0 233_MGAB_id_9_Synthetic_tr_25000_1st_41300\n",
- "2 0.817596 0.0 233_MGAB_id_9_Synthetic_tr_25000_1st_41300\n",
- "3 0.831398 0.0 233_MGAB_id_9_Synthetic_tr_25000_1st_41300\n",
- "4 0.846866 0.0 233_MGAB_id_9_Synthetic_tr_25000_1st_41300"
+ " Data Label FileName\n",
+ "0 0.166970 0.0 280_NEK_id_4_WebService_tr_500_1st_231\n",
+ "1 0.085052 0.0 280_NEK_id_4_WebService_tr_500_1st_231\n",
+ "2 0.080585 0.0 280_NEK_id_4_WebService_tr_500_1st_231\n",
+ "3 0.100995 0.0 280_NEK_id_4_WebService_tr_500_1st_231\n",
+ "4 0.092652 0.0 280_NEK_id_4_WebService_tr_500_1st_231"
]
},
- "execution_count": 47,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view first timeseries plot\n",
- "df_univariate_first_entry = df_univariate.where(df_univariate['FileName'] == '233_MGAB_id_9_Synthetic_tr_25000_1st_41300').dropna()\n",
+ "df_univariate_first_entry = df_univariate.where(df_univariate['FileName'] == '280_NEK_id_4_WebService_tr_500_1st_231').dropna()\n",
"df_univariate_first_entry.head()"
]
},
@@ -458,16 +458,16 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(80000, 3)"
+ "(1073, 3)"
]
},
- "execution_count": 30,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -478,17 +478,15 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\express\\_core.py:1985: FutureWarning:\n",
- "\n",
- "When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
- "\n"
+ "c:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\plotly\\express\\_core.py:1985: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+ " sf: grouped.get_group(s if len(s) > 1 else s[0])\n"
]
},
{
@@ -744,10 +742,6 @@
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}
@@ -201225,7 +3369,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -201235,7 +3379,7 @@
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn[32], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m path_multivariate \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata/TSB-AD-M/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 2\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m019_MITDB_id_1_Medical_tr_37500_1st_103211.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m df_multivariate_single \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_multivariate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m df_multivariate_single\u001b[38;5;241m.\u001b[39mhead()\n",
+ "Cell \u001b[1;32mIn[15], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m path_multivariate \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata/TSB-AD-M/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 2\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m019_MITDB_id_1_Medical_tr_37500_1st_103211.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m df_multivariate_single \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_multivariate\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m df_multivariate_single\u001b[38;5;241m.\u001b[39mhead()\n",
"File \u001b[1;32mc:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
"File \u001b[1;32mc:\\Users\\desiw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
@@ -201254,7 +3398,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -201274,7 +3418,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -201483,7 +3627,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
diff --git a/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb b/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
index 04eaba108dc766df87b7d27e44ec998818bbc662..adada2ee0357bd3a6168f7029babf2e0c9052a17 100644
--- a/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
+++ b/src/group_evaluation/create_combined_dataframe_with_metrics_and_plot.ipynb
@@ -315,12 +315,12 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"colors = {\n",
- " 'CNN': 'deepskyblue',\n",
+ " 'CNN': 'steelblue',\n",
" 'IForest':'darkmagenta' ,\n",
" 'KMeansAD_U': 'darkseagreen' ,\n",
" 'LOF':'darksalmon',#'darkgoldenrod',\n",
@@ -338,7 +338,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -2811,7 +2811,7 @@
"hovertemplate": "Model=%{x}<br>AUC-PR=%{y}<extra></extra>",
"legendgroup": "CNN",
"marker": {
- "color": "deepskyblue"
+ "color": "steelblue"
},
"name": "CNN",
"notched": false,
diff --git a/src/models/desi/SAND.py b/src/models/desi/SAND.py
index b242aca1f71a13c44229266ead2f839fa4d71bd4..e8ba3202336546781c60144225099491dcb2d5f4 100644
--- a/src/models/desi/SAND.py
+++ b/src/models/desi/SAND.py
@@ -133,6 +133,9 @@ class SAND():
join = np.nan_to_num(join)
all_join.append(join)
+ if len(all_join) < 1:
+ return []
+
join = [0]*len(all_join[0])
for sub_join,scores_sub_join,scores_sub_join_old,t_decay in zip(all_join,self.weights,self.nm_current_weight,self.time_decay):
@@ -275,8 +278,11 @@ class SAND():
all_subsequences.append(self.ts[i:i+self.subsequence_length])
idxs.append(i)
+ X2_subs = np.array(all_subsequences)
+ if len(X2_subs.shape) != 2:
+ return [],[]
ks = KShape(n_clusters=self.k,verbose=False)
- list_label = ks.fit_predict(np.array(all_subsequences))
+ list_label = ks.fit_predict(X2_subs)
cluster_subseq = [[] for i in range(self.k)]
@@ -311,7 +317,11 @@ class SAND():
if j != i:
dist_nms += self._sbd(nm[0],nm_t[0])[0]
Centrality.append(dist_nms)
-
+
+ if len(Frequency) < 1:
+ self.weights = []
+ self.time_decay = []
+ return
Frequency = list((np.array(Frequency) - min(Frequency))/(max(Frequency) - min(Frequency)+0.0000001)+1)
Centrality = list((np.array(Centrality) - min(Centrality))/(max(Centrality) - min(Centrality)+0.0000001)+1)
diff --git a/src/models/desi/call_kShapeAD.py b/src/models/desi/call_kShapeAD.py
index 5715dc2aaae46f8c4aaa10504742075c6151d802..5b54615696f3123fe6cb8af6d235463d3f84836f 100644
--- a/src/models/desi/call_kShapeAD.py
+++ b/src/models/desi/call_kShapeAD.py
@@ -7,8 +7,10 @@ if BASE_DIR not in sys.path:
sys.path.append(BASE_DIR)
from src.utils.slidingWindows import find_length_rank
from src.run_model_wrapper import main
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.simplefilter(action='ignore', category=UserWarning)
-#optimal hyperparameters from autors: 'POLY': {'periodicity': 1, 'power': 4}
params = {
'periodicity': [1, 2, 3]
}
diff --git a/src/run_model_wrapper.py b/src/run_model_wrapper.py
index b72cb8dd1cfa3592a917d69343bbfe134ff479ca..bfab9d9bf62e39e1bedfc2fbb391ae1984915b90 100644
--- a/src/run_model_wrapper.py
+++ b/src/run_model_wrapper.py
@@ -80,13 +80,14 @@ def hyperparameter_optimization(run_model, data,label,train_data, hyperparams,sl
# 'Recall': float
# }'''
- metrics = get_metrics(scores, labels=label, slidingWindow=slidingWindow)
- auc_pr = metrics.get("AUC-PR", 0)# get value from auc-pr
+ if len(scores) > 0:
+ metrics = get_metrics(scores, labels=label, slidingWindow=slidingWindow)
+ auc_pr = metrics.get("AUC-PR", 0)# get value from auc-pr
- #check if score is better as best score
- if auc_pr > best_score:
- best_score = auc_pr
- best_params = params
+ #check if score is better as best score
+ if auc_pr > best_score:
+ best_score = auc_pr
+ best_params = params
return best_params
@@ -261,11 +262,11 @@ def main(run_model, hyperparams, model_name, data_folders = '../../../data/', mo
#go through all files in train data and store additionally informations about group and anomaly types
grid_List = []
- # print(f"Start Processing files")
+ print(f"Start Processing files")
#reutrns [{file_name:str,train_data:list,data:list, label:list, sliding_window:int}]
file_data_dict_list = preprocess_data(path_data_all)
- # print('Start Hyperparameter Tuning')
+ print('Start Hyperparameter Tuning')
# Creates combinations of all hyperparameters
param_combinations = list(itertools.product(*hyperparams.values()))
#find hyperparametrs for each file:
@@ -278,21 +279,20 @@ def main(run_model, hyperparams, model_name, data_folders = '../../../data/', mo
# Hyperparameter-Optimierung
best_params = hyperparameter_optimization(run_model, data,label,train_data, hyperparams,sliding_window,param_combinations,model_type)
- # print(f"File: {filename}, best hyperparameter: {best_params}")
#grid_list looks like this: [{ params: {key:value}, file_name:str,train_data:list,data:list, label:list, sliding_window:int}]
grid_List.append({'file_name':filename,'params':best_params,'train_data':train_data,'data':data,'label':label,'sliding_window':sliding_window})
- # print('Start Training and evaluation')
+ print('Start Training and evaluation')
results_list = train_test_model(run_model,grid_List,path_to_folder, model_type)
#convert list of dicts to a dict with lists
results = create_dict_from_array(results_list)
#check duration time
duration = sum(results['duration'])
- # print(f'Job done. Duration: {duration:.3f} seconds')
+ print(f'Job done. Duration: {duration:.3f} seconds')
#store results for each model by folder
results_df = pd.DataFrame(results)
output_csv = os.path.join(output_dir,model_name + '.csv')
results_df.to_csv(output_csv, index=False)
- # print(f"Ergebnisse gespeichert in: {output_csv}")
+ print(f"Ergebnisse gespeichert in: {output_csv}")