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recommender_system
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4fa668fe
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4fa668fe
rédigé
1 year ago
par
Audrey Ghilain
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evaluator.ipynb
+8
-40
8 ajouts, 40 suppressions
evaluator.ipynb
avec
8 ajouts
et
40 suppressions
evaluator.ipynb
+
8
−
40
Voir le fichier @
4fa668fe
...
...
@@ -59,7 +59,7 @@
},
{
"cell_type": "code",
"execution_count":
10
,
"execution_count":
2
,
"id": "d6d82188",
"metadata": {},
"outputs": [],
...
...
@@ -193,7 +193,7 @@
},
{
"cell_type": "code",
"execution_count":
11
,
"execution_count":
5
,
"id": "f1849e55",
"metadata": {},
"outputs": [],
...
...
@@ -246,51 +246,19 @@
},
{
"cell_type": "code",
"execution_count":
12
,
"execution_count":
6
,
"id": "704f4d2a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Handling model baseline_1\n",
"Training split predictions\n",
"- computing metric mae\n",
"- computing metric rmse\n",
"Training loo predictions\n",
"Training full predictions\n",
"Handling model baseline_2\n",
"Training split predictions\n",
"- computing metric mae\n",
"- computing metric rmse\n",
"Training loo predictions\n",
"Training full predictions\n",
"Handling model baseline_3\n",
"Training split predictions\n",
"- computing metric mae\n",
"- computing metric rmse\n",
"Training loo predictions\n",
"Training full predictions\n",
"Handling model baseline_4\n",
"Training split predictions\n",
"- computing metric mae\n",
"- computing metric rmse\n",
"Training loo predictions\n",
"Training full predictions\n",
"Handling model ContentBased\n"
]
},
{
"ename": "TypeError",
"evalue": "ContentBased.__init__() missing 2 required positional arguments: 'features_method' and 'regressor_method'",
"ename": "NameError",
"evalue": "name 'accuracy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m sp_ratings \u001b[38;5;241m=\u001b[39m load_ratings(surprise_format\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 18\u001b[0m precomputed_dict \u001b[38;5;241m=\u001b[39m precomputed_information(pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/tiny/evidence/ratings.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m---> 19\u001b[0m evaluation_report \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_evaluation_report\u001b[49m\u001b[43m(\u001b[49m\u001b[43mEvalConfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msp_ratings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecomputed_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mAVAILABLE_METRICS\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m export_evaluation_report(evaluation_report)\n",
"Cell \u001b[0;32mIn[10], line 81\u001b[0m, in \u001b[0;36mcreate_evaluation_report\u001b[0;34m(eval_config, sp_ratings, precomputed_dict, available_metrics)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model_name, model, arguments \u001b[38;5;129;01min\u001b[39;00m eval_config\u001b[38;5;241m.\u001b[39mmodels:\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHandling model \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 81\u001b[0m algo \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m evaluation_dict[model_name] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# Type 1 : split evaluations\u001b[39;00m\n",
"\u001b[0;31mTypeError\u001b[0m: ContentBased.__init__() missing 2 required positional arguments: 'features_method' and 'regressor_method'"
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m AVAILABLE_METRICS \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplit\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmae\u001b[39m\u001b[38;5;124m\"\u001b[39m: (\u001b[43maccuracy\u001b[49m\u001b[38;5;241m.\u001b[39mmae, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}),\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrmse\u001b[39m\u001b[38;5;124m\"\u001b[39m: (accuracy\u001b[38;5;241m.\u001b[39mrmse, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m})\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Add new split metrics here if needed\u001b[39;00m\n\u001b[1;32m 6\u001b[0m },\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloo\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhit_rate\u001b[39m\u001b[38;5;124m\"\u001b[39m: (get_hit_rate, {}),\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Add new loo metrics here if needed\u001b[39;00m\n\u001b[1;32m 10\u001b[0m },\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfull\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnovelty\u001b[39m\u001b[38;5;124m\"\u001b[39m: (get_novelty, {}),\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# Add new full metrics here if needed\u001b[39;00m\n\u001b[1;32m 14\u001b[0m }\n\u001b[1;32m 15\u001b[0m }\n\u001b[1;32m 17\u001b[0m sp_ratings \u001b[38;5;241m=\u001b[39m load_ratings(surprise_format\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 18\u001b[0m precomputed_dict \u001b[38;5;241m=\u001b[39m precomputed_information(pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/tiny/evidence/ratings.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
"\u001b[0;31mNameError\u001b[0m: name 'accuracy' is not defined"
]
}
],
...
...
%% Cell type:markdown id:a665885b tags:
# Evaluator Module
The Evaluator module creates evaluation reports.
Reports contain evaluation metrics depending on models specified in the evaluation config.
%% Cell type:code id:6aaf9140 tags:
```
python
# reloads modules automatically before entering the execution of code
%
load_ext
autoreload
%
autoreload
2
# third parties imports
import
numpy
as
np
import
pandas
as
pd
# -- add new imports here --
# local imports
from
configs
import
EvalConfig
from
constants
import
Constant
as
C
from
loaders
import
export_evaluation_report
from
loaders
import
load_ratings
# -- add new imports here --
from
surprise.model_selection
import
train_test_split
from
surprise
import
accuracy
from
surprise.model_selection
import
LeaveOneOut
from
collections
import
Counter
```
%% Output
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
%% Cell type:markdown id:d47c24a4 tags:
# 1. Model validation functions
Validation functions are a way to perform crossvalidation on recommender system models.
%% Cell type:code id:d6d82188 tags:
```
python
def
generate_split_predictions
(
algo
,
ratings_dataset
,
eval_config
):
"""
Generate predictions on a random test set specified in eval_config
"""
# -- implement the function generate_split_predictions --
# Spliting the data into train and test sets
trainset
,
testset
=
train_test_split
(
ratings_dataset
,
test_size
=
eval_config
.
test_size
)
# Training the algorithm on the train data set
algo
.
fit
(
trainset
)
# Predict ratings for the testset
predictions
=
algo
.
test
(
testset
)
return
predictions
def
generate_loo_top_n
(
algo
,
ratings_dataset
,
eval_config
):
"""
Generate top-n recommendations for each user on a random Leave-one-out split (LOO)
"""
# -- implement the function generate_loo_top_n --
# Create a LeaveOneOut split
loo
=
LeaveOneOut
(
n_splits
=
1
)
for
trainset
,
testset
in
loo
.
split
(
ratings_dataset
):
algo
.
fit
(
trainset
)
# Train the algorithm on the training set
anti_testset
=
trainset
.
build_anti_testset
()
# Build the anti test-set
predictions
=
algo
.
test
(
anti_testset
)
# Get predictions on the anti test-set
top_n
=
{}
for
uid
,
iid
,
_
,
est
,
_
in
predictions
:
if
uid
not
in
top_n
:
top_n
[
uid
]
=
[]
top_n
[
uid
].
append
((
iid
,
est
))
for
uid
,
user_ratings
in
top_n
.
items
():
user_ratings
.
sort
(
key
=
lambda
x
:
x
[
1
],
reverse
=
True
)
top_n
[
uid
]
=
user_ratings
[:
eval_config
.
top_n_value
]
# Get top-N recommendations
anti_testset_top_n
=
top_n
return
anti_testset_top_n
,
testset
def
generate_full_top_n
(
algo
,
ratings_dataset
,
eval_config
):
"""
Generate top-n recommendations for each user with full training set (LOO)
"""
full_trainset
=
ratings_dataset
.
build_full_trainset
()
# Build the full training set
algo
.
fit
(
full_trainset
)
# Train the algorithm on the full training set
anti_testset
=
full_trainset
.
build_anti_testset
()
# Build the anti test-set
predictions
=
algo
.
test
(
anti_testset
)
# Get predictions on the anti test-set
top_n
=
{}
for
uid
,
iid
,
_
,
est
,
_
in
predictions
:
if
uid
not
in
top_n
:
top_n
[
uid
]
=
[]
top_n
[
uid
].
append
((
iid
,
est
))
for
uid
,
user_ratings
in
top_n
.
items
():
user_ratings
.
sort
(
key
=
lambda
x
:
x
[
1
],
reverse
=
True
)
top_n
[
uid
]
=
user_ratings
[:
eval_config
.
top_n_value
]
# Get top-N recommendations
anti_testset_top_n
=
top_n
return
anti_testset_top_n
def
precomputed_information
(
movie_data
):
"""
Returns a dictionary that precomputes relevant information for evaluating in full mode
Dictionary keys:
- precomputed_dict[
"
item_to_rank
"
] : contains a dictionary mapping movie ids to rankings
- (-- for your project, add other relevant information here -- )
"""
# Initialize an empty dictionary to store item_id to rank mapping
item_to_rank
=
{}
# Calculate popularity rank for each movie
ratings_count
=
movie_data
.
groupby
(
'
movieId
'
).
size
().
sort_values
(
ascending
=
False
)
# Assign ranks to movies based on their popularity
for
rank
,
(
movie_id
,
_
)
in
enumerate
(
ratings_count
.
items
(),
start
=
1
):
item_to_rank
[
movie_id
]
=
rank
# Create the precomputed dictionary
precomputed_dict
=
{}
precomputed_dict
[
"
item_to_rank
"
]
=
item_to_rank
return
precomputed_dict
def
create_evaluation_report
(
eval_config
,
sp_ratings
,
precomputed_dict
,
available_metrics
):
"""
Create a DataFrame evaluating various models on metrics specified in an evaluation config.
"""
evaluation_dict
=
{}
for
model_name
,
model
,
arguments
in
eval_config
.
models
:
print
(
f
'
Handling model
{
model_name
}
'
)
algo
=
model
(
**
arguments
)
evaluation_dict
[
model_name
]
=
{}
# Type 1 : split evaluations
if
len
(
eval_config
.
split_metrics
)
>
0
:
print
(
'
Training split predictions
'
)
predictions
=
generate_split_predictions
(
algo
,
sp_ratings
,
eval_config
)
for
metric
in
eval_config
.
split_metrics
:
print
(
f
'
- computing metric
{
metric
}
'
)
assert
metric
in
available_metrics
[
'
split
'
]
evaluation_function
,
parameters
=
available_metrics
[
"
split
"
][
metric
]
evaluation_dict
[
model_name
][
metric
]
=
evaluation_function
(
predictions
,
**
parameters
)
# Type 2 : loo evaluations
if
len
(
eval_config
.
loo_metrics
)
>
0
:
print
(
'
Training loo predictions
'
)
anti_testset_top_n
,
testset
=
generate_loo_top_n
(
algo
,
sp_ratings
,
eval_config
)
for
metric
in
eval_config
.
loo_metrics
:
assert
metric
in
available_metrics
[
'
loo
'
]
evaluation_function
,
parameters
=
available_metrics
[
"
loo
"
][
metric
]
evaluation_dict
[
model_name
][
metric
]
=
evaluation_function
(
anti_testset_top_n
,
testset
,
**
parameters
)
# Type 3 : full evaluations
if
len
(
eval_config
.
full_metrics
)
>
0
:
print
(
'
Training full predictions
'
)
anti_testset_top_n
=
generate_full_top_n
(
algo
,
sp_ratings
,
eval_config
)
for
metric
in
eval_config
.
full_metrics
:
assert
metric
in
available_metrics
[
'
full
'
]
evaluation_function
,
parameters
=
available_metrics
[
"
full
"
][
metric
]
evaluation_dict
[
model_name
][
metric
]
=
evaluation_function
(
anti_testset_top_n
,
**
precomputed_dict
,
**
parameters
)
return
pd
.
DataFrame
.
from_dict
(
evaluation_dict
).
T
```
%% Cell type:markdown id:f7e83d1d tags:
# 2. Evaluation metrics
Implement evaluation metrics for either rating predictions (split metrics) or for top-n recommendations (loo metric, full metric)
%% Cell type:code id:f1849e55 tags:
```
python
def
get_hit_rate
(
anti_testset_top_n
,
testset
):
"""
Compute the average hit over the users (loo metric)
A hit (1) happens when the movie in the testset has been picked by the top-n recommender
A fail (0) happens when the movie in the testset has not been picked by the top-n recommender
"""
# -- implement the function get_hit_rate --
hits
=
0
total_users
=
len
(
testset
)
for
uid
,
true_iid
,
_
in
testset
:
if
uid
in
anti_testset_top_n
and
true_iid
in
{
iid
for
iid
,
_
in
anti_testset_top_n
[
uid
]}:
hits
+=
1
hit_rate
=
hits
/
total_users
return
hit_rate
def
get_novelty
(
anti_testset_top_n
,
item_to_rank
):
"""
Compute the average novelty of the top-n recommendation over the users (full metric)
The novelty is defined as the average ranking of the movies recommended
"""
# -- implement the function get_novelty --
total_rank_sum
=
0
total_recommendations
=
0
for
uid
,
recommendations
in
anti_testset_top_n
.
items
():
for
iid
,
_
in
recommendations
:
if
iid
in
item_to_rank
:
total_rank_sum
+=
item_to_rank
[
iid
]
total_recommendations
+=
1
if
total_recommendations
==
0
:
return
0
# Avoid division by zero
average_rank_sum
=
total_rank_sum
/
total_recommendations
return
average_rank_sum
```
%% Cell type:markdown id:1a9855b3 tags:
# 3. Evaluation workflow
Load data, evaluate models and save the experimental outcomes
%% Cell type:code id:704f4d2a tags:
```
python
AVAILABLE_METRICS
=
{
"
split
"
:
{
"
mae
"
:
(
accuracy
.
mae
,
{
'
verbose
'
:
False
}),
"
rmse
"
:
(
accuracy
.
rmse
,
{
'
verbose
'
:
False
})
# Add new split metrics here if needed
},
"
loo
"
:
{
"
hit_rate
"
:
(
get_hit_rate
,
{}),
# Add new loo metrics here if needed
},
"
full
"
:
{
"
novelty
"
:
(
get_novelty
,
{}),
# Add new full metrics here if needed
}
}
sp_ratings
=
load_ratings
(
surprise_format
=
True
)
precomputed_dict
=
precomputed_information
(
pd
.
read_csv
(
"
data/tiny/evidence/ratings.csv
"
))
evaluation_report
=
create_evaluation_report
(
EvalConfig
,
sp_ratings
,
precomputed_dict
,
AVAILABLE_METRICS
)
export_evaluation_report
(
evaluation_report
)
```
%% Output
Handling model baseline_1
Training split predictions
- computing metric mae
- computing metric rmse
Training loo predictions
Training full predictions
Handling model baseline_2
Training split predictions
- computing metric mae
- computing metric rmse
Training loo predictions
Training full predictions
Handling model baseline_3
Training split predictions
- computing metric mae
- computing metric rmse
Training loo predictions
Training full predictions
Handling model baseline_4
Training split predictions
- computing metric mae
- computing metric rmse
Training loo predictions
Training full predictions
Handling model ContentBased
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[12], line 19
NameError Traceback (most recent call last)
Cell In[6], line 3
1 AVAILABLE_METRICS = {
2 "split": {
----> 3 "mae": (accuracy.mae, {'verbose': False}),
4 "rmse": (accuracy.rmse, {'verbose': False})
5 # Add new split metrics here if needed
6 },
7 "loo": {
8 "hit_rate": (get_hit_rate, {}),
9 # Add new loo metrics here if needed
10 },
11 "full": {
12 "novelty": (get_novelty, {}),
13 # Add new full metrics here if needed
14 }
15 }
17 sp_ratings = load_ratings(surprise_format=True)
18 precomputed_dict = precomputed_information(pd.read_csv("data/tiny/evidence/ratings.csv"))
---> 19 evaluation_report = create_evaluation_report(EvalConfig, sp_ratings, precomputed_dict, AVAILABLE_METRICS)
20 export_evaluation_report(evaluation_report)
Cell In[10], line 81, in create_evaluation_report(eval_config, sp_ratings, precomputed_dict, available_metrics)
79 for model_name, model, arguments in eval_config.models:
80 print(f'Handling model {model_name}')
---> 81 algo = model(
**
arguments)
82 evaluation_dict[model_name] = {}
84 # Type 1 : split evaluations
TypeError: ContentBased.__init__() missing 2 required positional arguments: 'features_method' and 'regressor_method'
NameError: name 'accuracy' is not defined
...
...
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