{
  "id": 534940,
  "title": "Why i am getting a Submission Scoreing Error?",
  "url": "/competitions/ariel-data-challenge-2024/discussion/534940",
  "author_name": "Ayush Saxena",
  "post_date": "2024-09-19T11:12:03.478000",
  "votes": 1,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I have submitted over 4 notebooks, but still the problem persists. For the referance the code i have used lately is - </p>\n<p>`# Import necessary libraries<br>\nimport numpy as np<br>\nimport pandas as pd<br>\nimport os</p>\n<h1>Set the data directory</h1>\n<p>data_dir = '/kaggle/input/ariel-data-challenge-2024'  # Update if the directory name is different</p>\n<h1>Load metadata and sample submission</h1>\n<p>train_labels = pd.read_csv(os.path.join(data_dir, 'train_labels.csv'))<br>\nsample_submission = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))</p>\n<h1>Get the list of spectral and uncertainty column names from the sample submission</h1>\n<p>spectral_cols = [col for col in sample_submission.columns if col.startswith('wavelength_')]<br>\nuncertainty_cols = [col for col in sample_submission.columns if col.startswith('uncertainty_wavelength_')]</p>\n<p>print(\"Number of spectral columns in sample_submission:\", len(spectral_cols))<br>\nprint(\"Number of uncertainty columns in sample_submission:\", len(uncertainty_cols))</p>\n<h1>Extract the same spectral columns from train_labels (if available)</h1>\n<p>spectral_columns_in_train = [col for col in train_labels.columns if col in spectral_cols]<br>\nprint(\"Number of spectral columns in train_labels:\", len(spectral_columns_in_train))</p>\n<h1>Compute the mean spectrum from training labels (only for matching columns)</h1>\n<p>mean_spectrum = train_labels[spectral_columns_in_train].mean().values  # Shape: (num_spectral_columns,)</p>\n<h1>Set a fixed uncertainty</h1>\n<p>fixed_uncertainty = 100  # Adjust this value if needed<br>\nuncertainty_spectrum = np.full(len(spectral_cols), fixed_uncertainty)</p>\n<h1>Prepare the submission DataFrame</h1>\n<p>submission_df = pd.DataFrame()<br>\nsubmission_df['planet_id'] = sample_submission['planet_id']</p>\n<h1>Number of test samples</h1>\n<p>num_test_samples = submission_df.shape[0]<br>\nprint(\"Number of test samples:\", num_test_samples)</p>\n<h1>Repeat the mean_spectrum and uncertainty_spectrum for each test sample</h1>\n<p>predictions = np.tile(mean_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)<br>\nuncertainties = np.tile(uncertainty_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)</p>\n<h1>Verify that the number of columns matches</h1>\n<p>print(\"Predictions shape:\", predictions.shape)<br>\nprint(\"Uncertainties shape:\", uncertainties.shape)</p>\n<h1>Add predictions to the submission DataFrame</h1>\n<p>submission_df[spectral_cols] = predictions<br>\nsubmission_df[uncertainty_cols] = uncertainties</p>\n<h1>Verify the submission format</h1>\n<p>print(\"Submission DataFrame shape:\", submission_df.shape)<br>\nprint(\"First few rows:\")<br>\nprint(submission_df.head())</p>\n<h1>Save the submission file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)<br>\n`</p>\n<p>Let me know. What do you think?</p>",
  "messages": [
    {
      "id": 2993004,
      "postDate": "2024-09-19T11:12:03.480Z",
      "content": "<p>I have submitted over 4 notebooks, but still the problem persists. For the referance the code i have used lately is - </p>\n<p>`# Import necessary libraries<br>\nimport numpy as np<br>\nimport pandas as pd<br>\nimport os</p>\n<h1>Set the data directory</h1>\n<p>data_dir = '/kaggle/input/ariel-data-challenge-2024'  # Update if the directory name is different</p>\n<h1>Load metadata and sample submission</h1>\n<p>train_labels = pd.read_csv(os.path.join(data_dir, 'train_labels.csv'))<br>\nsample_submission = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))</p>\n<h1>Get the list of spectral and uncertainty column names from the sample submission</h1>\n<p>spectral_cols = [col for col in sample_submission.columns if col.startswith('wavelength_')]<br>\nuncertainty_cols = [col for col in sample_submission.columns if col.startswith('uncertainty_wavelength_')]</p>\n<p>print(\"Number of spectral columns in sample_submission:\", len(spectral_cols))<br>\nprint(\"Number of uncertainty columns in sample_submission:\", len(uncertainty_cols))</p>\n<h1>Extract the same spectral columns from train_labels (if available)</h1>\n<p>spectral_columns_in_train = [col for col in train_labels.columns if col in spectral_cols]<br>\nprint(\"Number of spectral columns in train_labels:\", len(spectral_columns_in_train))</p>\n<h1>Compute the mean spectrum from training labels (only for matching columns)</h1>\n<p>mean_spectrum = train_labels[spectral_columns_in_train].mean().values  # Shape: (num_spectral_columns,)</p>\n<h1>Set a fixed uncertainty</h1>\n<p>fixed_uncertainty = 100  # Adjust this value if needed<br>\nuncertainty_spectrum = np.full(len(spectral_cols), fixed_uncertainty)</p>\n<h1>Prepare the submission DataFrame</h1>\n<p>submission_df = pd.DataFrame()<br>\nsubmission_df['planet_id'] = sample_submission['planet_id']</p>\n<h1>Number of test samples</h1>\n<p>num_test_samples = submission_df.shape[0]<br>\nprint(\"Number of test samples:\", num_test_samples)</p>\n<h1>Repeat the mean_spectrum and uncertainty_spectrum for each test sample</h1>\n<p>predictions = np.tile(mean_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)<br>\nuncertainties = np.tile(uncertainty_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)</p>\n<h1>Verify that the number of columns matches</h1>\n<p>print(\"Predictions shape:\", predictions.shape)<br>\nprint(\"Uncertainties shape:\", uncertainties.shape)</p>\n<h1>Add predictions to the submission DataFrame</h1>\n<p>submission_df[spectral_cols] = predictions<br>\nsubmission_df[uncertainty_cols] = uncertainties</p>\n<h1>Verify the submission format</h1>\n<p>print(\"Submission DataFrame shape:\", submission_df.shape)<br>\nprint(\"First few rows:\")<br>\nprint(submission_df.head())</p>\n<h1>Save the submission file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)<br>\n`</p>\n<p>Let me know. What do you think?</p>",
      "rawMarkdown": "I have submitted over 4 notebooks, but still the problem persists. For the referance the code i have used lately is - \n\n`# Import necessary libraries\nimport numpy as np\nimport pandas as pd\nimport os\n\n# Set the data directory\ndata_dir = '/kaggle/input/ariel-data-challenge-2024'  # Update if the directory name is different\n\n# Load metadata and sample submission\ntrain_labels = pd.read_csv(os.path.join(data_dir, 'train_labels.csv'))\nsample_submission = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))\n\n# Get the list of spectral and uncertainty column names from the sample submission\nspectral_cols = [col for col in sample_submission.columns if col.startswith('wavelength_')]\nuncertainty_cols = [col for col in sample_submission.columns if col.startswith('uncertainty_wavelength_')]\n\nprint(\"Number of spectral columns in sample_submission:\", len(spectral_cols))\nprint(\"Number of uncertainty columns in sample_submission:\", len(uncertainty_cols))\n\n# Extract the same spectral columns from train_labels (if available)\nspectral_columns_in_train = [col for col in train_labels.columns if col in spectral_cols]\nprint(\"Number of spectral columns in train_labels:\", len(spectral_columns_in_train))\n\n# Compute the mean spectrum from training labels (only for matching columns)\nmean_spectrum = train_labels[spectral_columns_in_train].mean().values  # Shape: (num_spectral_columns,)\n\n# Set a fixed uncertainty\nfixed_uncertainty = 100  # Adjust this value if needed\nuncertainty_spectrum = np.full(len(spectral_cols), fixed_uncertainty)\n\n# Prepare the submission DataFrame\nsubmission_df = pd.DataFrame()\nsubmission_df['planet_id'] = sample_submission['planet_id']\n\n# Number of test samples\nnum_test_samples = submission_df.shape[0]\nprint(\"Number of test samples:\", num_test_samples)\n\n# Repeat the mean_spectrum and uncertainty_spectrum for each test sample\npredictions = np.tile(mean_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)\nuncertainties = np.tile(uncertainty_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)\n\n# Verify that the number of columns matches\nprint(\"Predictions shape:\", predictions.shape)\nprint(\"Uncertainties shape:\", uncertainties.shape)\n\n# Add predictions to the submission DataFrame\nsubmission_df[spectral_cols] = predictions\nsubmission_df[uncertainty_cols] = uncertainties\n\n# Verify the submission format\nprint(\"Submission DataFrame shape:\", submission_df.shape)\nprint(\"First few rows:\")\nprint(submission_df.head())\n\n# Save the submission file\nsubmission_df.to_csv('submission.csv', index=False)\n`\n\n\nLet me know. What do you think?",
      "votes": 1
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2993004": "I have submitted over 4 notebooks, but still the problem persists. For the referance the code i have used lately is - \n\n`# Import necessary libraries\nimport numpy as np\nimport pandas as pd\nimport os\n\n# Set the data directory\ndata_dir = '/kaggle/input/ariel-data-challenge-2024'  # Update if the directory name is different\n\n# Load metadata and sample submission\ntrain_labels = pd.read_csv(os.path.join(data_dir, 'train_labels.csv'))\nsample_submission = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))\n\n# Get the list of spectral and uncertainty column names from the sample submission\nspectral_cols = [col for col in sample_submission.columns if col.startswith('wavelength_')]\nuncertainty_cols = [col for col in sample_submission.columns if col.startswith('uncertainty_wavelength_')]\n\nprint(\"Number of spectral columns in sample_submission:\", len(spectral_cols))\nprint(\"Number of uncertainty columns in sample_submission:\", len(uncertainty_cols))\n\n# Extract the same spectral columns from train_labels (if available)\nspectral_columns_in_train = [col for col in train_labels.columns if col in spectral_cols]\nprint(\"Number of spectral columns in train_labels:\", len(spectral_columns_in_train))\n\n# Compute the mean spectrum from training labels (only for matching columns)\nmean_spectrum = train_labels[spectral_columns_in_train].mean().values  # Shape: (num_spectral_columns,)\n\n# Set a fixed uncertainty\nfixed_uncertainty = 100  # Adjust this value if needed\nuncertainty_spectrum = np.full(len(spectral_cols), fixed_uncertainty)\n\n# Prepare the submission DataFrame\nsubmission_df = pd.DataFrame()\nsubmission_df['planet_id'] = sample_submission['planet_id']\n\n# Number of test samples\nnum_test_samples = submission_df.shape[0]\nprint(\"Number of test samples:\", num_test_samples)\n\n# Repeat the mean_spectrum and uncertainty_spectrum for each test sample\npredictions = np.tile(mean_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)\nuncertainties = np.tile(uncertainty_spectrum, (num_test_samples, 1))  # Shape: (num_test_samples, num_spectral_columns)\n\n# Verify that the number of columns matches\nprint(\"Predictions shape:\", predictions.shape)\nprint(\"Uncertainties shape:\", uncertainties.shape)\n\n# Add predictions to the submission DataFrame\nsubmission_df[spectral_cols] = predictions\nsubmission_df[uncertainty_cols] = uncertainties\n\n# Verify the submission format\nprint(\"Submission DataFrame shape:\", submission_df.shape)\nprint(\"First few rows:\")\nprint(submission_df.head())\n\n# Save the submission file\nsubmission_df.to_csv('submission.csv', index=False)\n`\n\n\nLet me know. What do you think?"
  }
}