"* Ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.\n",
"* Alt+ENTER evaluates the current cell and adds a new cell below it.\n",
"* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two \"d\"s in a row) when the frame is blue."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GRADING\n",
"\n",
"You will be graded on parts that are marked with **\\#TODO** comments. Read the comments in the code to make sure you don't miss any.\n",
"| Modify `preprocess` for normalization | 5 | 10 |\n",
"\n",
"\n",
"Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There a total of 25 points in this assignment and extra 5 bonus points for 478 students."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Supervised Learning Model Skeleton\n",
"\n",
"We'll use this skeleton for implementing different supervised learning algorithms. For this first assignment, we'll read and partition the [\"madelon\" dataset](http://archive.ics.uci.edu/ml/datasets/madelon). Features and labels for the first two examples are listed below. Please complete \"preprocess\" and \"partition\" methods. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 500 features in the \"madelon\" dataset have integer values:"
"This step is for reading the dataset and for extracting features and labels. The \"preprocess\" function should return an $n \\times d$ \"features\" array, and an $n \\times 1$ \"labels\" array, where $n$ is the number of examples and $d$ is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def preprocess(feature_file, label_file):\n",
" '''\n",
" Args:\n",
" feature_file: str \n",
" file containing features\n",
" label_file: str\n",
" file containing labels\n",
" Returns:\n",
" features: ndarray\n",
" nxd features\n",
" labels: ndarray\n",
" nx1 labels\n",
" '''\n",
" # You might find np.genfromtxt useful for reading in the file. Be careful with the file delimiter, \n",
" # e.g. for comma-separated files use delimiter=',' argument.\n",
"# TODO: Output the dimension of both features and labels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TASK 2: Implement `partition`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, you'll need to split your dataset into training, validation and test sets. The \"partition\" function should take as input the size of the whole dataset and randomly sample a proportion $t$ of the dataset as test partition and a proportion of $v$ as validation partition. The remaining will be used as training data. For example, to keep 30% of the examples as test and %10 as validation, set $t=0.3$ and $v=0.1$. You should choose these values according to the size of the data available to you. The \"split\" function should return indices of the training, validation and test sets. These will be used to index into the whole training set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def partition(size, t, v = 0):\n",
" '''\n",
" Args:\n",
" size: int\n",
" number of examples in the whole dataset\n",
" t: float\n",
" proportion kept for test\n",
" v: float\n",
" proportion kept for validation\n",
" Returns:\n",
" test_indices: ndarray\n",
" 1D array containing test set indices\n",
" val_indices: ndarray\n",
" 1D array containing validation set indices\n",
" '''\n",
" \n",
" # np.random.permutation might come in handy. Do not sample with replacement!\n",
" # Be sure not to use the same indices in test and validation sets!\n",
" \n",
" # use the first np.ceil(size*t) for test, \n",
" # the following np.ceil(size*v) for validation set.\n",
" \n",
" # TODO\n",
" \n",
" raise NotImplementedError\n",
" \n",
" return test_indices, val_indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rubric:\n",
"* Correct length of test indices +5, +2.5\n",
"* Correct length of validation indices +5, +2.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test `partition`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO\n",
"# Pass the correct size argument (number of examples in the whole dataset)\n",
"test_indices, val_indices = partition(size=..., t = 0.3, v = 0.1)\n",
"\n",
"# Output the size of both features and labels."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TASK 3: Putting things together"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model definition is given below. We'll extend this class for different supervised classification algorithms. Specifically, we'll implement \"fit\" and \"predict\" methods for these algorithms. For this assignment, you are not asked to implement these methods. Run the cells below and make sure each piece of code fits together and works as expected."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Model:\n",
" # preprocess_f and partition_f expect functions\n",
" # use kwargs to pass arguments to preprocessor_f and partition_f\n",
" # kwargs is a dictionary and should contain t, v, feature_file, label_file\n",
"We will use a keyword arguments dictionary that conveniently passes arguments to functions that are themselves passed as arguments during object initialization. Please do not change these calls in this and the following assignments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO\n",
"# pass the correct arguments to preprocessor_f and partition_f\n",
"Modify `preprocess` function such that the output features take values in the range [0, 1]. Initialize a new model with this function and check the values of the features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rubric:\n",
"* Correct range for feature values +5, +10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO\n",
"# args is a placeholder for the parameters of the function\n",
"# Check that the range of each feature in the training set is in range [0, 1]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:markdown id: tags:
# JUPYTER NOTEBOOK TIPS
Each rectangular box is called a cell.
* Ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
* Alt+ENTER evaluates the current cell and adds a new cell below it.
* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
%% Cell type:markdown id: tags:
# GRADING
You will be graded on parts that are marked with **\#TODO** comments. Read the comments in the code to make sure you don't miss any.
| Modify `preprocess` for normalization | 5 | 10 |
Points are broken down further below in Rubric sections. The **first** score is for 478, the **second** is for 878 students. There a total of 25 points in this assignment and extra 5 bonus points for 478 students.
%% Cell type:markdown id: tags:
# Supervised Learning Model Skeleton
We'll use this skeleton for implementing different supervised learning algorithms. For this first assignment, we'll read and partition the ["madelon" dataset](http://archive.ics.uci.edu/ml/datasets/madelon). Features and labels for the first two examples are listed below. Please complete "preprocess" and "partition" methods.
%% Cell type:markdown id: tags:
The 500 features in the "madelon" dataset have integer values:
This step is for reading the dataset and for extracting features and labels. The "preprocess" function should return an $n \times d$ "features" array, and an $n \times 1$ "labels" array, where $n$ is the number of examples and $d$ is the number of features in the dataset. In cases where there is a big difference between the scales of features, we want to normalize the features to have values in the same range [0,1]. Since this is not the case with this dataset, we will not do normalization.
%% Cell type:code id: tags:
``` python
defpreprocess(feature_file,label_file):
'''
Args:
feature_file: str
file containing features
label_file: str
file containing labels
Returns:
features: ndarray
nxd features
labels: ndarray
nx1 labels
'''
# You might find np.genfromtxt useful for reading in the file. Be careful with the file delimiter,
# e.g. for comma-separated files use delimiter=',' argument.
# TODO: Output the dimension of both features and labels.
```
%% Cell type:markdown id: tags:
## TASK 2: Implement `partition`
%% Cell type:markdown id: tags:
Next, you'll need to split your dataset into training, validation and test sets. The "partition" function should take as input the size of the whole dataset and randomly sample a proportion $t$ of the dataset as test partition and a proportion of $v$ as validation partition. The remaining will be used as training data. For example, to keep 30% of the examples as test and %10 as validation, set $t=0.3$ and $v=0.1$. You should choose these values according to the size of the data available to you. The "split" function should return indices of the training, validation and test sets. These will be used to index into the whole training set.
%% Cell type:code id: tags:
``` python
defpartition(size,t,v=0):
'''
Args:
size: int
number of examples in the whole dataset
t: float
proportion kept for test
v: float
proportion kept for validation
Returns:
test_indices: ndarray
1D array containing test set indices
val_indices: ndarray
1D array containing validation set indices
'''
# np.random.permutation might come in handy. Do not sample with replacement!
# Be sure not to use the same indices in test and validation sets!
# use the first np.ceil(size*t) for test,
# the following np.ceil(size*v) for validation set.
# TODO
raiseNotImplementedError
returntest_indices,val_indices
```
%% Cell type:markdown id: tags:
### Rubric:
* Correct length of test indices +5, +2.5
* Correct length of validation indices +5, +2.5
%% Cell type:markdown id: tags:
### Test `partition`
%% Cell type:code id: tags:
``` python
# TODO
# Pass the correct size argument (number of examples in the whole dataset)
The model definition is given below. We'll extend this class for different supervised classification algorithms. Specifically, we'll implement "fit" and "predict" methods for these algorithms. For this assignment, you are not asked to implement these methods. Run the cells below and make sure each piece of code fits together and works as expected.
%% Cell type:code id: tags:
``` python
classModel:
# preprocess_f and partition_f expect functions
# use kwargs to pass arguments to preprocessor_f and partition_f
# kwargs is a dictionary and should contain t, v, feature_file, label_file
# e.g. {'t': 0.3, 'v': 0.1, 'feature_file': 'some_file_name', 'label_file': 'some_file_name'}
We will use a keyword arguments dictionary that conveniently passes arguments to functions that are themselves passed as arguments during object initialization. Please do not change these calls in this and the following assignments.
%% Cell type:code id: tags:
``` python
# TODO
# pass the correct arguments to preprocessor_f and partition_f
Modify `preprocess` function such that the output features take values in the range [0, 1]. Initialize a new model with this function and check the values of the features.
%% Cell type:markdown id: tags:
### Rubric:
* Correct range for feature values +5, +10
%% Cell type:code id: tags:
``` python
# TODO
# args is a placeholder for the parameters of the function
