apply style to notebooks

ibm-granite · Aug 2, 2024 · a48b04f · a48b04f
1 parent 38ca85c
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diff --git a/notebooks/hfdemo/patch_tsmixer_blog.ipynb b/notebooks/hfdemo/patch_tsmixer_blog.ipynb
@@ -96,28 +96,27 @@
    "source": [
     "# Standard\n",
     "import os\n",
-    "import random\n",
+    "\n",
+    "# supress some warnings\n",
+    "import warnings\n",
+    "\n",
+    "import pandas as pd\n",
     "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
     "    PatchTSMixerConfig,\n",
     "    PatchTSMixerForPrediction,\n",
-    "    set_seed,\n",
     "    Trainer,\n",
     "    TrainingArguments,\n",
+    "    set_seed,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",
     "from tsfm_public.toolkit.time_series_preprocessor import TimeSeriesPreprocessor\n",
     "from tsfm_public.toolkit.util import select_by_index\n",
     "\n",
-    "# supress some warnings\n",
-    "import warnings\n",
     "\n",
     "warnings.filterwarnings(\"ignore\", module=\"torch\")"
    ]
@@ -916,9 +915,7 @@
    ],
    "source": [
     "print(\"Loading pretrained model\")\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer_4/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer_4/electricity/model/pretrain/\")\n",
     "print(\"Done\")"
    ]
   },
@@ -1323,9 +1320,7 @@
    ],
    "source": [
     "# Reload the model\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer_4/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer_4/electricity/model/pretrain/\")\n",
     "finetune_forecast_trainer = Trainer(\n",
     "    model=finetune_forecast_model,\n",
     "    args=finetune_forecast_args,\n",

diff --git a/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb b/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb
@@ -32,9 +32,12 @@
    ],
    "source": [
     "# Standard\n",
-    "import os\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
@@ -43,9 +46,6 @@
     "    Trainer,\n",
     "    TrainingArguments,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",
@@ -321,9 +321,7 @@
    ],
    "source": [
     "print(\"Loading pretrained model\")\n",
-    "inference_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"ibm-granite/granite-timeseries-patchtsmixer\"\n",
-    ")\n",
+    "inference_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-patchtsmixer\")\n",
     "print(\"Done\")"
    ]
   },

diff --git a/notebooks/hfdemo/patch_tsmixer_transfer.ipynb b/notebooks/hfdemo/patch_tsmixer_transfer.ipynb
@@ -49,6 +49,10 @@
     "import os\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
@@ -57,9 +61,6 @@
     "    Trainer,\n",
     "    TrainingArguments,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",
@@ -923,9 +924,7 @@
    ],
    "source": [
     "print(\"Loading pretrained model\")\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer/electricity/model/pretrain/\")\n",
     "print(\"Done\")"
    ]
   },
@@ -1415,9 +1414,7 @@
    ],
    "source": [
     "# Reload the model\n",
-    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\n",
-    "    \"patchtsmixer/electricity/model/pretrain/\"\n",
-    ")\n",
+    "finetune_forecast_model = PatchTSMixerForPrediction.from_pretrained(\"patchtsmixer/electricity/model/pretrain/\")\n",
     "finetune_forecast_trainer = Trainer(\n",
     "    model=finetune_forecast_model,\n",
     "    args=finetune_forecast_args,\n",

diff --git a/notebooks/hfdemo/patch_tst_getting_started.ipynb b/notebooks/hfdemo/patch_tst_getting_started.ipynb
@@ -19,9 +19,12 @@
    "outputs": [],
    "source": [
     "# Standard\n",
-    "import os\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
@@ -30,9 +33,6 @@
     "    Trainer,\n",
     "    TrainingArguments,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",
@@ -309,9 +309,7 @@
    ],
    "source": [
     "print(\"Loading pretrained model\")\n",
-    "inference_forecast_model = PatchTSTForPrediction.from_pretrained(\n",
-    "    \"ibm-granite/granite-timeseries-patchtst\"\n",
-    ")\n",
+    "inference_forecast_model = PatchTSTForPrediction.from_pretrained(\"ibm-granite/granite-timeseries-patchtst\")\n",
     "print(\"Done\")"
    ]
   },

diff --git a/notebooks/hfdemo/patch_tst_transfer.ipynb b/notebooks/hfdemo/patch_tst_transfer.ipynb
@@ -35,6 +35,10 @@
     "import os\n",
     "import random\n",
     "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
     "# Third Party\n",
     "from transformers import (\n",
     "    EarlyStoppingCallback,\n",
@@ -43,9 +47,6 @@
     "    Trainer,\n",
     "    TrainingArguments,\n",
     ")\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
     "\n",
     "# First Party\n",
     "from tsfm_public.toolkit.dataset import ForecastDFDataset\n",

diff --git a/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb b/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb
@@ -35,24 +35,24 @@
     "# Standard\n",
     "import math\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
     "# Third Party\n",
     "from torch.optim import AdamW\n",
     "from torch.optim.lr_scheduler import OneCycleLR\n",
     "from transformers import EarlyStoppingCallback, Trainer, TrainingArguments, set_seed\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
     "\n",
-    "# First Party\n",
-    "from tsfm_public.toolkit.callbacks import TrackingCallback\n",
+    "# Local\n",
+    "from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction\n",
     "from tsfm_public.models.tinytimemixer.utils import (\n",
     "    count_parameters,\n",
     "    get_data,\n",
     "    plot_preds,\n",
     ")\n",
     "\n",
-    "# Local\n",
-    "from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction\n"
+    "# First Party\n",
+    "from tsfm_public.toolkit.callbacks import TrackingCallback"
    ]
   },
   {
@@ -84,13 +84,13 @@
     "\n",
     "# Make sure all the datasets in the following `list_datasets` are\n",
     "# saved in the `DATA_ROOT_PATH` folder. Or, change it accordingly.\n",
-    "# Refer to the get_data() function \n",
-    "# in notebooks/hfdemo/tinytimemixer/utils/ttm_utils.py \n",
+    "# Refer to the get_data() function\n",
+    "# in notebooks/hfdemo/tinytimemixer/utils/ttm_utils.py\n",
     "# to see how it is used.\n",
     "DATA_ROOT_PATH = \"datasets/\"\n",
     "\n",
     "# This is where results will be saved\n",
-    "OUT_DIR = \"ttm_results_benchmark_1024_96_tmp\"\n"
+    "OUT_DIR = \"ttm_results_benchmark_1024_96_tmp\""
    ]
   },
   {
@@ -115,7 +115,7 @@
     "    \"weather\",\n",
     "    \"electricity\",\n",
     "    \"traffic\",\n",
-    "]\n"
+    "]"
    ]
   },
   {
@@ -138,9 +138,7 @@
     "elif context_length == 1024:\n",
     "    hf_model_branch = \"1024_96_v1\"\n",
     "else:\n",
-    "    raise ValueError(\n",
-    "        \"Current supported context lengths are 512 and 1024. Stay tuned for more TTMs!\"\n",
-    "    )\n"
+    "    raise ValueError(\"Current supported context lengths are 512 and 1024. Stay tuned for more TTMs!\")"
    ]
   },
   {
@@ -195,9 +193,7 @@
     "    ##### Use the pretrained model in zero-shot forecasting #####\n",
     "    #############################################################\n",
     "    # Load model\n",
-    "    zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(\n",
-    "        hf_model_path, revision=hf_model_branch\n",
-    "    )\n",
+    "    zeroshot_model = TinyTimeMixerForPrediction.from_pretrained(hf_model_path, revision=hf_model_branch)\n",
     "\n",
     "    # zeroshot_trainer\n",
     "    zeroshot_trainer = Trainer(\n",
@@ -242,7 +238,7 @@
     "            context_length,\n",
     "            forecast_length,\n",
     "            fewshot_fraction=fewshot_percent / 100,\n",
-    "            data_root_path=DATA_ROOT_PATH\n",
+    "            data_root_path=DATA_ROOT_PATH,\n",
     "        )\n",
     "\n",
     "        # change head dropout to 0.7 for ett datasets\n",
@@ -343,20 +339,14 @@
     "\n",
     "        # write results\n",
     "        all_results[f\"fs{fewshot_percent}_mse\"].append(fewshot_output[\"eval_loss\"])\n",
-    "        all_results[f\"fs{fewshot_percent}_mean_epoch_time\"].append(\n",
-    "            tracking_callback.mean_epoch_time\n",
-    "        )\n",
-    "        all_results[f\"fs{fewshot_percent}_total_train_time\"].append(\n",
-    "            tracking_callback.total_train_time\n",
-    "        )\n",
-    "        all_results[f\"fs{fewshot_percent}_best_val_metric\"].append(\n",
-    "            tracking_callback.best_eval_metric\n",
-    "        )\n",
+    "        all_results[f\"fs{fewshot_percent}_mean_epoch_time\"].append(tracking_callback.mean_epoch_time)\n",
+    "        all_results[f\"fs{fewshot_percent}_total_train_time\"].append(tracking_callback.total_train_time)\n",
+    "        all_results[f\"fs{fewshot_percent}_best_val_metric\"].append(tracking_callback.best_eval_metric)\n",
     "\n",
     "    df_out = pd.DataFrame(all_results).round(3)\n",
     "    print(df_out[[\"dataset\", \"zs_mse\", \"fs5_mse\", \"fs10_mse\"]])\n",
     "    df_out.to_csv(f\"{OUT_DIR}/results_zero_few.csv\")\n",
-    "    df_out.to_csv(f\"{OUT_DIR}/results_zero_few.csv\")\n"
+    "    df_out.to_csv(f\"{OUT_DIR}/results_zero_few.csv\")"
    ]
   },
   {
@@ -544,7 +534,7 @@
     }
    ],
    "source": [
-    "df_out\n"
+    "df_out"
    ]
   },
   {