chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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name: Titanic-Captum-Example
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python_env: python_env.yaml
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entry_points:
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main:
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parameters:
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max_epochs: {type: int, default: 50}
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lr: {type: float, default: 0.1}
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command: |
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python Titanic_Captum_Interpret.py \
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--max_epochs {max_epochs} \
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--lr {lr}
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## Using Captum and MLflow to interpret Pytorch models
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In this example, we will demonstrate the basic features of the [Captum](https://captum.ai/) interpretability,and logging those features using mlflow library through an example model trained on the Titanic survival data.
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We will first train a deep neural network on the data using PyTorch and use Captum to understand which of the features were most important and how the network reached its prediction.
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you can get more details about used attributions methods used in this example
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1. [Titanic_Basic_Interpret](https://captum.ai/tutorials/Titanic_Basic_Interpret)
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2. [integrated-gradients](https://captum.ai/docs/algorithms#primary-attribution)
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3. [layer-attributions](https://captum.ai/docs/algorithms#layer-attribution)
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### Running the code
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To run the example via MLflow, navigate to the `mlflow/examples/pytorch/CaptumExample` directory and run the command
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```
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mlflow run .
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```
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This will run `Titanic_Captum_Interpret.py` with default parameter values, e.g. `--max_epochs=100` and `--use_pretrained_model False`. You can see the full set of parameters in the `MLproject` file within this directory.
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In order to run the file with custom parameters, run the command
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```
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mlflow run . -P max_epochs=X
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```
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where `X` is your desired value for `max_epochs`.
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If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument `--env-manager=local`.
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```
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mlflow run . --env-manager=local
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```
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### Viewing results in the MLflow UI
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Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command
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```
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mlflow server
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```
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and navigating to [http://localhost:5000](http://localhost:5000).
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For more details on MLflow tracking, see [the docs](https://www.mlflow.org/docs/latest/tracking.html#mlflow-tracking).
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### Passing custom training parameters
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The parameters can be overridden via the command line:
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1. max_epochs - Number of epochs to train model. Training can be interrupted early via Ctrl+C
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2. lr - Learning rate
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3. use_pretrained_model - If want to use pretrained model
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For example:
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```
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mlflow run . -P max_epochs=5 -P learning_rate=0.01 -P use_pretrained_model=True
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```
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Or to run the training script directly with custom parameters:
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```sh
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python Titanic_Captum_Interpret.py \
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--max_epochs 50 \
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--lr 0.1
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```
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## Logging to a custom tracking server
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To configure MLflow to log to a custom (non-default) tracking location, set the MLFLOW_TRACKING_URI environment variable, e.g. via export MLFLOW_TRACKING_URI=http://localhost:5000/. For more details, see [the docs](https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded).
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"""
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Getting started with Captum - Titanic Data Analysis
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"""
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# Initial imports
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import os
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from argparse import ArgumentParser
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import torch
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from captum.attr import IntegratedGradients, LayerConductance, NeuronConductance
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from prettytable import PrettyTable
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from scipy import stats
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from sklearn.model_selection import train_test_split
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from torch import nn
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import mlflow
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def get_titanic():
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"""
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we now preprocess the data by converting some categorical features such as
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gender, location of embarcation, and passenger class into one-hot encodings
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We also remove some features that are more difficult to analyze
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After processing, the features we have are:
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Age: Passenger Age
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Sibsp: Number of Siblings / Spouses Aboard
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Parch: Number of Parents / Children Aboard
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Fare: Fare Amount Paid in British Pounds
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Female: Binary variable indicating whether passenger is female
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Male: Binary variable indicating whether passenger is male
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EmbarkC : Binary var indicating whether passenger embarked @ Cherbourg
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EmbarkQ : Binary var indicating whether passenger embarked @ Queenstown
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EmbarkS : Binary var indicating whether passenger embarked @ Southampton
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Class1 : Binary var indicating whether passenger was in first class
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Class2 : Binary var indicating whether passenger was in second class
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Class3 : Binary var indicating whether passenger was in third class
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"""
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data_path = "titanic3.csv"
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titanic_data = pd.read_csv(data_path)
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titanic_data = pd.concat(
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[
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titanic_data,
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pd.get_dummies(titanic_data["sex"], dtype=np.uint8),
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pd.get_dummies(titanic_data["embarked"], prefix="embark", dtype=np.uint8),
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pd.get_dummies(titanic_data["pclass"], prefix="class", dtype=np.uint8),
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],
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axis=1,
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)
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titanic_data["age"] = titanic_data["age"].fillna(titanic_data["age"].mean())
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titanic_data["fare"] = titanic_data["fare"].fillna(titanic_data["fare"].mean())
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return titanic_data.drop(
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[
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"passengerid",
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"name",
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"ticket",
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"cabin",
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"sex",
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"embarked",
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"pclass",
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],
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axis=1,
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)
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torch.manual_seed(1) # Set seed for reproducibility.
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class TitanicSimpleNNModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = nn.Linear(12, 12)
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self.sigmoid1 = nn.Sigmoid()
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self.linear2 = nn.Linear(12, 8)
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self.sigmoid2 = nn.Sigmoid()
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self.linear3 = nn.Linear(8, 2)
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self.softmax = nn.Softmax(dim=1)
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def forward(self, x):
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lin1_out = self.linear1(x)
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sigmoid_out1 = self.sigmoid1(lin1_out)
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sigmoid_out2 = self.sigmoid2(self.linear2(sigmoid_out1))
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return self.softmax(self.linear3(sigmoid_out2))
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def prepare():
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RANDOM_SEED = 42
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titanic_data = get_titanic()
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print(titanic_data)
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labels = titanic_data["survived"].to_numpy()
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titanic_data = titanic_data.drop(["survived"], axis=1)
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feature_names = list(titanic_data.columns)
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data = titanic_data.to_numpy()
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# Separate training and test sets using
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train_features, test_features, train_labels, test_labels = train_test_split(
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data, labels, test_size=0.3, random_state=RANDOM_SEED, stratify=labels
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)
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train_features = np.vstack(train_features[:, :]).astype(np.float32)
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test_features = np.vstack(test_features[:, :]).astype(np.float32)
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return train_features, train_labels, test_features, test_labels, feature_names
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def count_model_parameters(model):
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table = PrettyTable(["Modules", "Parameters"])
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total_params = 0
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for name, parameter in model.named_parameters():
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if not parameter.requires_grad:
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continue
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param = parameter.nonzero(as_tuple=False).size(0)
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table.add_row([name, param])
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total_params += param
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return table, total_params
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def visualize_importances(
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feature_names,
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importances,
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title="Average Feature Importances",
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plot=True,
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axis_title="Features",
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):
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print(title)
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feature_imp = PrettyTable(["feature_name", "importances"])
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feature_imp_dict = {}
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for i in range(len(feature_names)):
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print(feature_names[i], ": ", f"{importances[i]:.3f}")
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feature_imp.add_row([feature_names[i], importances[i]])
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feature_imp_dict[str(feature_names[i])] = importances[i]
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x_pos = np.arange(len(feature_names))
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if plot:
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fig, ax = plt.subplots(figsize=(12, 6))
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ax.bar(x_pos, importances, align="center")
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ax.set(title=title, xlabel=axis_title)
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ax.set_xticks(x_pos)
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ax.set_xticklabels(feature_names, rotation="vertical")
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mlflow.log_figure(fig, title + ".png")
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return feature_imp, feature_imp_dict
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def train(USE_PRETRAINED_MODEL=False):
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net = TitanicSimpleNNModel()
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train_features, train_labels, test_features, test_labels, feature_names = prepare()
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USE_PRETRAINED_MODEL = dict_args["use_pretrained_model"]
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if USE_PRETRAINED_MODEL:
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net.load_state_dict(torch.load("models/titanic_state_dict.pt"))
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net.eval()
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print("Model Loaded!")
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else:
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criterion = nn.CrossEntropyLoss()
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num_epochs = dict_args["max_epochs"]
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mlflow.log_param("epochs", num_epochs)
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mlflow.log_param("lr", dict_args["lr"])
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optimizer = torch.optim.Adam(net.parameters(), lr=dict_args["lr"])
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print(train_features.dtype)
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input_tensor = torch.from_numpy(train_features).type(torch.FloatTensor)
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label_tensor = torch.from_numpy(train_labels)
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for epoch in range(num_epochs):
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output = net(input_tensor)
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loss = criterion(output, label_tensor)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if epoch % 50 == 0:
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print(f"Epoch {epoch + 1}/{num_epochs} => Train Loss: {loss.item():.2f}")
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mlflow.log_metric(
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f"Epoch {epoch + 1!s} Loss",
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float(loss.item()),
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step=epoch,
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)
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if not os.path.isdir("models"):
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os.makedirs("models")
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torch.save(net.state_dict(), "models/titanic_state_dict.pt")
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summary, _ = count_model_parameters(net)
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mlflow.log_text(str(summary), "model_summary.txt")
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return (
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net,
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train_features,
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train_labels,
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test_features,
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test_labels,
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feature_names,
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)
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def compute_accuracy(net, features, labels, title=None):
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input_tensor = torch.from_numpy(features).type(torch.FloatTensor)
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out_probs = net(input_tensor).detach().numpy()
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out_classes = np.argmax(out_probs, axis=1)
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mlflow.log_metric(title, float(sum(out_classes == labels) / len(labels)))
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print(title, sum(out_classes == labels) / len(labels))
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return input_tensor
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def feature_conductance(net, test_input_tensor):
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"""
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The method takes tensor(s) of input examples (matching the forward function of the model),
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and returns the input attributions for the given input example.
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The returned values of the attribute method are the attributions,
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which match the size of the given inputs, and delta,
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which approximates the error between the approximated integral and true integral.
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This method saves the distribution of avg attributions of the trained features for the given target.
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"""
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ig = IntegratedGradients(net)
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test_input_tensor.requires_grad_()
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attr, _ = ig.attribute(test_input_tensor, target=1, return_convergence_delta=True)
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attr = attr.detach().numpy()
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# To understand these attributions, we can first average them across all the inputs and print and visualize the average attribution for each feature.
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feature_imp, feature_imp_dict = visualize_importances(feature_names, np.mean(attr, axis=0))
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mlflow.log_metrics(feature_imp_dict)
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mlflow.log_text(str(feature_imp), "feature_imp_summary.txt")
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fig, (ax1, ax2) = plt.subplots(2, 1)
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fig.tight_layout(pad=3)
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ax1.hist(attr[:, 1], 100)
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ax1.set(title="Distribution of Sibsp Attribution Values")
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# we can bucket the examples by the value of the sibsp feature and plot the average attribution for the feature.
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# In the plot below, the size of the dot is proportional to the number of examples with that value.
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bin_means, bin_edges, _ = stats.binned_statistic(
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test_features[:, 1], attr[:, 1], statistic="mean", bins=6
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)
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bin_count, _, _ = stats.binned_statistic(
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test_features[:, 1], attr[:, 1], statistic="count", bins=6
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)
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bin_width = bin_edges[1] - bin_edges[0]
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bin_centers = bin_edges[1:] - bin_width / 2
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ax2.scatter(bin_centers, bin_means, s=bin_count)
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ax2.set(xlabel="Average Sibsp Feature Value", ylabel="Average Attribution")
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mlflow.log_figure(fig, "Average_Sibsp_Feature_Value.png")
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def layer_conductance(net, test_input_tensor):
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"""
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To use Layer Conductance, we create a LayerConductance object passing in the model as well as the module (layer) whose output we would like to understand.
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In this case, we choose net.sigmoid1, the output of the first hidden layer.
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Now obtain the conductance values for all the test examples by calling attribute on the LayerConductance object.
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LayerConductance also requires a target index for networks with multiple outputs, defining the index of the output for which gradients are computed.
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Similar to feature attributions, we provide target = 1, corresponding to survival.
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LayerConductance also utilizes a baseline, but we simply use the default zero baseline as in integrated gradients.
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"""
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cond = LayerConductance(net, net.sigmoid1)
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cond_vals = cond.attribute(test_input_tensor, target=1)
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cond_vals = cond_vals.detach().numpy()
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# We can begin by visualizing the average conductance for each neuron.
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neuron_names = ["neuron " + str(x) for x in range(12)]
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avg_neuron_imp, neuron_imp_dict = visualize_importances(
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neuron_names,
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np.mean(cond_vals, axis=0),
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title="Average Neuron Importances",
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axis_title="Neurons",
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)
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mlflow.log_metrics(neuron_imp_dict)
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mlflow.log_text(str(avg_neuron_imp), "neuron_imp_summary.txt")
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# We can also look at the distribution of each neuron's attributions. Below we look at the distributions for neurons 7 and 9,
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# and we can confirm that their attribution distributions are very close to 0, suggesting they are not learning substantial features.
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 6))
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fig.tight_layout(pad=3)
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ax1.hist(cond_vals[:, 9], 100)
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ax1.set(title="Neuron 9 Distribution")
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ax2.hist(cond_vals[:, 7], 100)
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ax2.set(title="Neuron 7 Distribution")
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mlflow.log_figure(fig, "Neurons_Distribution.png")
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def neuron_conductance(net, test_input_tensor, neuron_selector=None):
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"""
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We have identified that some of the neurons are not learning important features, while others are.
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Can we now understand what each of these important neurons are looking at in the input?
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For instance, are they identifying different features in the input or similar ones?
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To answer these questions, we can apply the third type of attributions available in Captum, **Neuron Attributions**.
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This allows us to understand what parts of the input contribute to activating a particular input neuron. For this example,
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we will apply Neuron Conductance, which divides the neuron's total conductance value into the contribution from each individual input feature.
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To use Neuron Conductance, we create a NeuronConductance object, analogously to Conductance,
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passing in the model as well as the module (layer) whose output we would like to understand, in this case, net.sigmoid1, as before.
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"""
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neuron_selector = 0
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neuron_cond = NeuronConductance(net, net.sigmoid1)
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# We can now obtain the neuron conductance values for all the test examples by calling attribute on the NeuronConductance object.
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# Neuron Conductance requires the neuron index in the target layer for which attributions are requested as well as the target index for networks with multiple outputs,
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# similar to layer conductance. As before, we provide target = 1, corresponding to survival, and compute neuron conductance for neurons 0 and 10, the significant neurons identified above.
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# The neuron index can be provided either as a tuple or as just an integer if the layer output is 1-dimensional.
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neuron_cond_vals = neuron_cond.attribute(
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test_input_tensor, neuron_selector=neuron_selector, target=1
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)
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neuron_cond, _ = visualize_importances(
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feature_names,
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neuron_cond_vals.mean(dim=0).detach().numpy(),
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title=f"Average Feature Importances for Neuron {neuron_selector}",
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)
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mlflow.log_text(
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str(neuron_cond), "Avg_Feature_Importances_Neuron_" + str(neuron_selector) + ".txt"
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)
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if __name__ == "__main__":
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parser = ArgumentParser(description="Titanic Captum Example")
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parser.add_argument(
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"--use_pretrained_model",
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default=False,
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metavar="N",
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help="Use pretrained model or train from the scratch",
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)
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parser.add_argument(
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"--max_epochs",
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type=int,
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default=100,
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metavar="N",
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help="Number of epochs to be used for training",
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)
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parser.add_argument(
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"--lr",
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type=float,
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default=0.1,
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metavar="LR",
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help="learning rate (default: 0.1)",
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)
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args = parser.parse_args()
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dict_args = vars(args)
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with mlflow.start_run(run_name="Titanic_Captum_mlflow"):
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net, train_features, train_labels, test_features, test_labels, feature_names = train()
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compute_accuracy(net, train_features, train_labels, title="Train Accuracy")
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test_input_tensor = compute_accuracy(net, test_features, test_labels, title="Test Accuracy")
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feature_conductance(net, test_input_tensor)
|
||||
layer_conductance(net, test_input_tensor)
|
||||
neuron_conductance(net, test_input_tensor)
|
||||
mlflow.log_param("Train Size", len(train_labels))
|
||||
mlflow.log_param("Test Size", len(test_labels))
|
||||
@@ -0,0 +1,12 @@
|
||||
build_dependencies:
|
||||
- pip
|
||||
dependencies:
|
||||
- mlflow
|
||||
- pandas
|
||||
- scipy
|
||||
- captum
|
||||
- boto3
|
||||
- scikit-learn
|
||||
- prettytable
|
||||
- ipython
|
||||
- torch
|
||||
@@ -0,0 +1,101 @@
|
||||
passengerid,survived,pclass,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked
|
||||
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
|
||||
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
|
||||
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
|
||||
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
|
||||
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
|
||||
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
|
||||
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
|
||||
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
|
||||
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
|
||||
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
|
||||
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
|
||||
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
|
||||
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
|
||||
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
|
||||
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
|
||||
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
|
||||
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
|
||||
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
|
||||
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
|
||||
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
|
||||
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
|
||||
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
|
||||
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
|
||||
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
|
||||
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
|
||||
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
|
||||
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
|
||||
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
|
||||
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
|
||||
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
|
||||
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
|
||||
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
|
||||
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
|
||||
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
|
||||
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
|
||||
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
|
||||
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
|
||||
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
|
||||
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
|
||||
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
|
||||
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
|
||||
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
|
||||
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
|
||||
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
|
||||
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
|
||||
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
|
||||
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
|
||||
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
|
||||
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
|
||||
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
|
||||
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
|
||||
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
|
||||
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
|
||||
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
|
||||
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
|
||||
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
|
||||
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
|
||||
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
|
||||
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
|
||||
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
|
||||
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
|
||||
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
|
||||
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
|
||||
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
|
||||
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
|
||||
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
|
||||
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
|
||||
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
|
||||
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
|
||||
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
|
||||
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
|
||||
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
|
||||
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
|
||||
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
|
||||
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
|
||||
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
|
||||
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
|
||||
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
|
||||
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
|
||||
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
|
||||
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
|
||||
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
|
||||
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
|
||||
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
|
||||
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
|
||||
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
|
||||
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
|
||||
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
|
||||
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
|
||||
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
|
||||
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
|
||||
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
|
||||
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
|
||||
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
|
||||
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
|
||||
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
|
||||
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
|
||||
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
|
||||
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
|
||||
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
|
||||
|
Reference in New Issue
Block a user