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# Population

A [`Population`](https://skfolio.org/generated/skfolio.population.Population.html.md#skfolio.population.Population) is a list of portfolios ([`Portfolio`](https://skfolio.org/generated/skfolio.portfolio.Portfolio.html.md#skfolio.portfolio.Portfolio)
or [`MultiPeriodPortfolio`](https://skfolio.org/generated/skfolio.portfolio.MultiPeriodPortfolio.html.md#skfolio.portfolio.MultiPeriodPortfolio) or both).
`Population` inherits from the built-in `list` class and extends it by adding new
functionalities to improve portfolio manipulation and analysis.

**Example:**

In this example, we create a Population of 100 random Portfolios:

```python
from skfolio import (
    PerfMeasure,
    Population,
    Portfolio,
    RatioMeasure,
    RiskMeasure,
)
from skfolio.datasets import load_sp500_dataset
from skfolio.preprocessing import prices_to_returns
from skfolio.utils.stats import rand_weights

prices = load_sp500_dataset()
X = prices_to_returns(X=prices)

population = Population([])

n_assets = X.shape[1]
for i in range(100):
    weights = rand_weights(n=n_assets)
    portfolio = Portfolio(X=X, weights=weights, name=str(i))
    population.append(portfolio)
```

Let’s explore some of the methods:

```python
print(population.composition())

print(population.summary())

portfolio = population.quantile(measure=RiskMeasure.VARIANCE, q=0.95)

population.set_portfolio_params(compounded=True)

fronts = population.non_dominated_sort()

population.plot_measures(
    x=RiskMeasure.ANNUALIZED_VARIANCE,
    y=PerfMeasure.ANNUALIZED_MEAN,
    z=RiskMeasure.MAX_DRAWDOWN,
    show_fronts=True,
)

population[:2].plot_cumulative_returns()

population.plot_distribution(
    measure_list=[RatioMeasure.SHARPE_RATIO, RatioMeasure.SORTINO_RATIO]
)

population.plot_composition()
```

A `Population` is returned by the `predict` method of some portfolio optimization that
supports multi-outputs.

For example, fitting [`MeanRisk`](https://skfolio.org/generated/skfolio.optimization.MeanRisk.html.md#skfolio.optimization.MeanRisk) with parameter
`efficient_frontier_size=30` will find the weights of 30 portfolios belonging to the
efficient frontier. Calling the method `predict(X_test)` on that model will return a
`Population` containing these 30 `Portfolio`, predicted on the test set:

```python
from sklearn.model_selection import train_test_split

from skfolio import (
    RiskMeasure,
)
from skfolio.datasets import load_sp500_dataset
from skfolio.optimization import MeanRisk
from skfolio.preprocessing import prices_to_returns

prices = load_sp500_dataset()
X = prices_to_returns(X=prices)
X_train, X_test = train_test_split(X, test_size=0.33, shuffle=False)

model = MeanRisk(
    risk_measure=RiskMeasure.VARIANCE,
    efficient_frontier_size=30,
)
model.fit(X_train)
print(model.weights_.shape)

population = model.predict(X_test)
```